Published OnlineFirst October 20, 2017; DOI: 10.1158/2159-8290.CD-17-0461
RESEARCH BRIEF
Suppression of Adaptive Responses
to Targeted Cancer Therapy by
Transcriptional Repression
Maria Rusan1,2,3, Kapsok Li1,4, Yvonne Li1,3, Camilla L. Christensen1, Brian J. Abraham5,
Nicholas Kwiatkowski5,6,7, Kevin A. Buczkowski1, Bruno Bockorny1,3,8, Ting Chen1, Shuai Li1, Kevin Rhee1,
Haikuo Zhang1, Wankun Chen1,9,10, Hideki Terai1, Tiffany Tavares1, Alan L. Leggett6, Tianxia Li1,
Yichen Wang1, Tinghu Zhang6,7, Tae-Jung Kim1,11, Sook-Hee Hong1, Neermala Poudel-Neupane1,
Michael Silkes1, Tenny Mudianto1, Li Tan6,7, Takeshi Shimamura12, Matthew Meyerson1,3,
Adam J. Bass1,3,13, Hideo Watanabe14, Nathanael S. Gray6,7, Richard A. Young5,15, Kwok-Kin Wong16,
and Peter S. Hammerman1,3,17
Acquired drug resistance is a major factor limiting the effectiveness of targeted
cancer therapies. Targeting tumors with kinase inhibitors induces complex adaptive programs that promote the persistence of a fraction of the original cell population, facilitating the
eventual outgrowth of inhibitor-resistant tumor clones. We show that the addition of a newly identified CDK7/12 inhibitor, THZ1, to targeted therapy enhances cell killing and impedes the emergence of
drug-resistant cell populations in diverse cellular and in vivo cancer models. We propose that targeted
therapy induces a state of transcriptional dependency in a subpopulation of cells poised to become
drug tolerant, which THZ1 can exploit by blocking dynamic transcriptional responses, promoting
remodeling of enhancers and key signaling outputs required for tumor cell survival in the setting of
targeted therapy. These findings suggest that the addition of THZ1 to targeted therapies is a promising
broad-based strategy to hinder the emergence of drug-resistant cancer cell populations.
ABSTRACT
SIGNIFICANCE: CDK7/12 inhibition prevents active enhancer formation at genes, promoting resistance emergence in response to targeted therapy, and impedes the engagement of transcriptional
programs required for tumor cell survival. CDK7/12 inhibition in combination with targeted cancer
therapies may serve as a therapeutic paradigm for enhancing the effectiveness of targeted therapies.
Cancer Discov; 8(1); 59–73. ©2017 AACR.
See related commentary by Carugo and Draetta, p. 17.
1
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston,
Massachusetts. 2Department of Clinical Medicine, Aarhus University, Aarhus,
Denmark. 3Cancer Program, Broad Institute of Harvard and Massachusetts
Institute of Technology, Cambridge, Massachusetts. 4Department of Dermatology, Chung-Ang University College of Medicine, Seoul, Korea. 5Whitehead
Institute for Biomedical Research, Cambridge, Massachusetts. 6Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts. 7Department of Biological Chemistry and Molecular Pharmacology,
Harvard Medical School, Boston, Massachusetts. 8Division of Hematology
and Oncology, Beth Israel Deaconess Medical Center, Boston, Massachusetts. 9Department of Anesthesiology, Fudan University Shanghai Cancer
Center, Shanghai, China. 10Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. 11Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea.
12
Molecular Pharmacology and Therapeutics, Oncology Research Institute,
Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois.
13
Departments of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts. 14Department of Medicine, Division of
Pulmonary, Critical Care and Sleep Medicine and Tisch Cancer Institute,
Icahn School of Medicine at Mount Sinai, New York, New York. 15Department
of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts. 16Laura & Isaac Perlmutter Cancer Center, NYU Langone Medical
Center, New York, New York. 17Novartis Institutes of Biomedical Research,
Cambridge, Massachusetts.
Note: Supplementary data for this article are available at Cancer Discovery
Online (http://cancerdiscovery.aacrjournals.org/).
Corresponding Author: Peter S. Hammerman, Novartis Institutes of
Biomedical Research, Cambridge, MA 02139. Phone: 617-632-6335; Fax:
617-582-7880; E-mail: peter.hammerman@novartis.com
doi: 10.1158/2159-8290.CD-17-0461
©2017 American Association for Cancer Research.
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Rusan et al.
RESEARCH BRIEF
INTRODUCTION
RESULTS
Large-scale genomic proiling efforts have facilitated the
characterization of molecular alterations in cancers and aided
the development of targeted kinase inhibitors for a wide
array of cancer types. However, resistance to these targeted
therapies invariably develops and limits their clinical eficacy
(1–3). Resistance often emerges following an initial period
of drug responsiveness via clonal evolution of the cancer
cell population. This may entail acquisition of treatmentrefractory mutations in the original target (4, 5), reactivation
of key downstream effectors of the targeted pathway (6, 7),
activation of alternative signaling pathways (8, 9), or cell state
changes (10) which render the cell population indifferent to
the original therapy. The emergence of acquired resistance
is facilitated by the rapid induction of a complex network
of prosurvival and proproliferative pathways upon exposure
to targeted therapy (11–13), collectively promoting the persistence of a fraction of the original population in a drugtolerant state (14) and permitting the eventual outgrowth of
resistant clones.
We hypothesized that repression of the transcriptional
changes that underlie the adaptive prosurvival and proproliferative responses induced by targeted therapy would
interfere with the establishment of the drug-tolerant state,
resulting in improved therapeutic eficacy. This would be
advantageous clinically, as it would circumvent having to
anticipate, elucidate, and target the myriad of potential
drug resistance mechanisms that might arise in a particular
patient. To test our hypothesis, we employed a novel transcriptional repressor, THZ1 (15). THZ1 is a covalent CDK7
inhibitor, which additionally targets CDK12 at higher doses
(15). CDK7 is a key regulator of the cell cycle (16–18),
and together with CDK12, regulates RNA polymerase II
(RNAPII)–mediated transcription (19–23). Prior studies
have identiied subsets of cancers with marked sensitivity to
THZ1 monotherapy (T-cell acute lymphoblastic leukemia,
ref. 15; MYCN-dependent neuroblastoma, ref. 24; small cell
lung cancer, ref. 25; and triple-negative breast cancer, ref.
26). Vulnerability to THZ1 has been shown to be mediated
by a strong dependency on speciic oncogenes regulated by
superenhancer elements, and their corresponding transcriptional circuits (i.e., RUNX in acute lymphoblastic leukemia,
MYCN in neuroblastoma, and in the case of triple-negative
breast cancer a core set of genes termed the “Achilles cluster”; refs. 15, 24, 26). Importantly, THZ1 has been shown
to be typically well tolerated in mouse models, suggesting
a therapeutic index for this agent in combination therapy
approaches (15, 24–26). Here, we sought to test whether
there may be a basis for therapeutic synergy among THZ1
and targeted cancer therapies in a variety of oncogenedriven cancer models, and speciically whether transcriptional CDK inhibition may be a novel therapeutic approach
to impede the ability of cancer cells to persist in the setting
of targeted cancer therapy. For ease of reading, we use the
term “targeted therapy” here to refer to small-molecule
inhibitors of receptor tyrosine kinases and additional signal transduction kinases (e.g., MEK1/2, BRAF). THZ1 as
an inhibitor of CDK7 could also be classiied as targeted
therapy, although we do not refer to it as such herein.
60 | CANCER DISCOVERY
January 2018
THZ1 Suppresses the Emergence of Resistant Cell
Populations In Vitro
To determine whether THZ1 can suppress the emergence
of resistant cancer cell populations, we irst performed colony formation assays in vitro in a panel of well-established
oncogene dependency models with diverse receptor tyrosine
kinase dependencies and lineages (Fig. 1A and B; Supplementary Figs. S1a and S2a). Of note, despite low nanomolar
potency of targeted agents in the models employed (Supplementary Fig. S1a), resistant cell populations were readily
detected by 4 weeks of kinase inhibitor treatment, underscoring the need for strategies that further enhance the
eficacy of targeted therapies (Fig. 1A and B; Supplementary
Fig. S2a). The models employed exhibited variable sensitivity
to THZ1 monotherapy; however, in the majority of models
high-dose THZ1 (e.g., 1 µmol/L or higher) eliminated all or
nearly all cells at 96 hours (Supplementary Fig. S1b), as THZ1
acts as a general suppressor of transcription at high doses,
akin to actinomycin (15). To avoid general toxicity associated with high-dose THZ1 and to assess for therapeutic
synergy with targeted kinase inhibitors, we employed sublethal low nanomolar doses, corresponding approximately
to the IC50 of THZ1 (Supplementary Fig. S1a). This dose of
THZ1 approximated the minimal dose, which produced a
robust response in combination with targeted kinase inhibitors. At these sublethal doses, colony formation at 4 weeks
with THZ1 treatment alone was comparable to control.
Colony formation with targeted tyrosine kinase inhibition
alone had variable effect with up to 90% reduction in colony
formation compared with control depending on the cellular
model, however yielded resistant colonies in all models (Fig.
1A and B; Supplementary Fig. S2a). In contrast, combination
treatment with THZ1 and targeted kinase inhibition yielded
few or no detectable colonies. Combination treatment also
signiicantly enhanced cell death at an early time point (48
hours) compared with either single agent alone (Fig. 1C;
Supplementary Fig. S2b). We observed an equally striking
effect when treating with THZ1 in combination with a MEK
inhibitor (trametinib) in KRAS-mutant non–small cell lung
cancer (NSCLC) or gastric cancer cellular models, and with
THZ1 in combination with a BRAF inhibitor (vemurafenib)
in a BRAF-mutant melanoma model (Fig. 1D–F; Supplementary Fig. S1a and S1b and Supplementary Fig. S2a).
Additionally, colony formation results at 4 weeks were maintained in long-term assays with no detectable drug-resistant
colonies noted in combination-treated wells at 3 months
(Supplementary Fig. S2c). We considered the speciicity of
the observed synergy by performing colony formation assays
with THZ1 in combination with kinase inhibitors not targeting the dependency of the cellular model tested [e.g., A549, a
KRAS-dependent model, was treated with crizotinib (a MET/
ALK inhibitor) instead of trametinib], and we found that
combination treatment with a mismatched kinase inhibitor
plus THZ1 had no effect on colony outgrowth (Supplementary Fig. S2d). Taken together, these data suggest that THZ1
broadly has the ability to prevent resistance emergence to
targeted kinase inhibition in diverse genetic contexts and
lineages.
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Published OnlineFirst October 20, 2017; DOI: 10.1158/2159-8290.CD-17-0461
CDK7/12 Inhibition Prevents Resistance to Targeted Therapy
B
RT112
DMSO
BGJ
PC9
DMSO
Erlo
H3122
DMSO
Drug-resistant colonies
(% of control, at 4 weeks)
A
RESEARCH BRIEF
120
***
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80
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40
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E
A549
DMSO
Tram
H23
DMSO
Tram
H1792
DMSO
Tram
Drug-resistant colonies
(% of control, at 4 weeks)
D
Cell death
(% of total cells, at 48 hrs)
C
100
80
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(% of total cells, at 48 hrs)
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+
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Figure 1. THZ1 in combination with targeted therapy enhances cell death and hinders the establishment of drug-resistant colonies in diverse oncogene-addicted cellular models. A, Receptor tyrosine kinase–dependent cell lines, RT112 (FGFR), PC9 (EGFR), and H3122 (ALK) were treated with DMSO,
the corresponding tyrosine kinase inhibitor [TKI: BGJ398 (BGJ), erlotinib (Erlo), or crizotinib (Criz)], THZ1, or THZ1 in combination with the corresponding
TKI. Colony formation was assayed by crystal violet staining at 4 weeks. DMSO was stained by 1 week. Two representative wells from a minimum of three
biological replicates are shown per condition. (RT112: BGJ398 1 µmol/L, THZ1 100 nmol/L; PC9: erlotinib 1 µmol/L, THZ1 100 nmol/L; H3122: crizotinib
250 nmol/L, THZ1 50 nmol/L.) Note: Colony formation for RT112 with THZ1 at 150 nmol/L in combination with BGJ398 yielded no detectable colonies
at 4 weeks (Supplementary Fig. S2f). B, Quantification of colony formation in A, shown as a percentage of the control. Mean (3 biological replicates) ±
standard deviation (SD) shown (*, P < 0.05; **, < 0.005; ***, < 0.0005, two-sided t test). ND, not detectable. C, Cell death analysis with cells treated as in
A by flow cytometry with Annexin V/PI staining, following 48 hours of treatment. Mean (3 biological replicates) ± SD shown (*, P < 0.05; **, < 0.005; ***,
< 0.0005, two-sided t test, comparing total cell death; Annexin V+/PI− or PI+). Left, RT112; middle, PC9; right, H3122. D, KRAS-mutant cell lines A549,
H23, and H1792 were treated with DMSO, trametinib (Tram), THZ1, or a combination of THZ1 and trametinib. Colony formation was assayed by crystal
violet staining at 4 weeks. DMSO was stained by 1 week. Two representative wells from a minimum of three independent biological replicates are shown
per condition. (A549: trametinib 200 nmol/L, THZ1 150 nmol/L; H23: trametinib 500 nmol/L, THZ1 100 nmol/L; H1792: trametinib 500 nmol/L, THZ1
500 nmol/L). E, Quantification of colony formation in D, shown as a percentage of the control. Mean (3 biological replicates) ± SD shown (*, P < 0.05;
**, < 0.005; ***, < 0.0005, two-sided t test). ND, not detectable. F, Cell death analysis with cells treated as in D by flow cytometry with Annexin V/PI staining, following 48 hours of treatment. Mean (3 biological replicates) ± SD shown (*, P < 0.05; **, < 0.005; ***, < 0.0005, two-sided t test, comparing total cell
death; Annexin V+/PI− or PI+). Left, A549; middle, H23; right, H1792. Note: H23 had different drug response dynamics compared with the other cell lines
tested, with the cell death in the combination-treated group ensuing close to the 2-week time point.
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Rusan et al.
RESEARCH BRIEF
THZ1 as an Adjunct to Targeted Therapy May Offer
a More Broadly Applicable Strategy to Rational
Combination Therapy Approaches
We further compared our results with one of the prominent approaches for addressing resistance, namely rational
combination therapy employing two or more kinase inhibitors to simultaneously target both the driver oncogene and
previously identiied resistance mechanisms (3, 27–30). We
tested rational combination therapies in RT112, PC9, and
H3122 cells, using BGJ398, erlotinib, or crizotinib, respectively, in combination with agents targeting known resistance mechanisms for these cell lines (8, 31–36). As expected,
rational combination therapy decreased the proportion of
cells surviving acute treatment at 96 hours (Supplementary
Fig. S2e) and reduced the outgrowth of resistant clones with
variable success at 4 weeks and 3 months (Supplementary Fig.
S2f and S2g). Rational combination therapy with targeted
kinase inhibition and MEK or PI3K inhibition conferred the
greatest decrease in colony formation at 3 months across the
three models. Approaches utilizing these rational combinations have however been challenging to translate clinically
due to toxicity, which has compromised the ability to assess
their clinical eficacy (37–40). In contrast to the rational combination therapy approaches we tested, THZ1 in combination with targeted therapy consistently yielded no detectable
colonies across all tested backgrounds at time points up to 3
months (Supplementary Fig. S2f and S2g).
CDK7- or CDK12-Deficient Cells Display Enhanced
Sensitivity to Targeted Therapy
To conirm that the therapeutic effects noted with THZ1
are due to CDK7 and/or CDK12 inhibition, we tested
whether genetic depletion of CDK7 or CDK12 mimics
the effects of THZ1 treatment. We generated CDK7- or
CDK12-deicient PC9 cells using CRISPR/Cas9 gene editing
(Supplementary Fig. S3a and S3b). Both CDK7- and CDK12deicient PC9 cells displayed enhanced sensitivity to erlotinib
at 48 hours as compared with PC9 cells with a control RNA
guide (CDK7_12_dummy; Supplementary Fig. S3c). CDK12
depletion, however, had more modest effects. We furthermore
performed colony formation assays with CDK7- or CDK12deicient PC9 cells; however, the general cytotoxicity of CDK7
or CDK12 depletion precluded the performance of longerterm experiments. In an orthogonal approach, we performed
colony formation with THZ531, a recently described selective covalent CDK12 inhibitor (41). THZ531 demonstrated
therapeutic synergy with BGJ398 in RT112, and erlotinib
in PC9, corroborating the data from CDK12-deicient PC9
cells (Supplementary Fig. S3d and S3e). These indings suggest that the synergy noted with THZ1 in combination with
targeted therapies is conferred through inhibition of CDK7
and CDK12.
THZ1 in Combination with Targeted Therapy
Retards Tumor Growth and Improves
Survival In Vivo
To assess the eficacy and toxicity of targeted therapy in
combination with THZ1 in vivo, we performed xenograft
studies using cell-line models of FGFR-mutant bladder carcinoma (RT112) and EGFR-mutant NSCLC (PC9; Fig. 2A; Supplementary Fig. S4a). Tumor-bearing mice were treated with
(i) vehicle, (ii) BGJ398 (RT112) or erlotinib (PC9), (iii) THZ1,
or (iv) combination treatment with the appropriate targeted
therapy and THZ1. THZ1 in combination with targeted
therapy retarded tumor growth compared with THZ1 or
targeted therapy alone (Supplementary Fig. S4a), and signiicantly improved survival (Fig. 2A). Importantly, combination
therapy was well tolerated, with no weight loss or behavioral
changes observed (Supplementary Fig. S4d).
In addition, we tested THZ1 in combination with the covalent T790M-mutant-EGFR selective inhibitor WZ4002 (42),
in a novel EGFR-T790M-L858RLSL/−; Trp53-R172HLSL/− (TLP)
genetically engineered mouse model (GEMM) of NSCLC
(Fig. 2B). L858R is an activating mutation of EGFR, T790M
is a gatekeeper mutation, which confers decreased sensitivity to irst- and second-generation EGFR inhibitors, and
p53-R172H is a dominant negative (DN) p53 mutation that
is found in 38% of EGFR-mutant NSCLC and is associated
with more advanced, aggressive disease (43). Upon detectable
tumor burden by MRI, mice were randomized into treatment
groups (Fig. 2B). Thereafter, tumor growth was evaluated by
biweekly MRI. Treatment with WZ4002 resulted in initial
response at 2 weeks (P = 0.0117, two-tailed t test); however,
tumors rapidly developed resistance and rebounded by 4
weeks, reaching end-stage disease by 5 weeks of treatment,
emphasizing the aggressive nature of this EGFR-mutant,
p53-mutant GEMM. In stark contrast, combined THZ1WZ4002 treatment resulted in a dramatic response with
extensive long-term tumor regression (Fig. 2C and D). Mice
in the combination arms continued to have signiicant tumor
regression at 14 weeks of treatment (Fig. 2C, right). Furthermore, combination-treated mice had 100% survival versus 0%
survival for single-agent treated mice at 14 weeks (P = 0.0019,
log-rank test; Fig. 2E), and no overt toxicity was evident in the
Figure 2. THZ1 in combination with targeted therapy increases survival in xenograft models and a novel EGFR-T790M-L858RLSL/−; Trp53-R172HLSL/−
NSCLC GEMM. A, Xenografts of RT112 and PC9 tumors were treated with the indicated drugs for 8 weeks (n = 5 mice in each treatment group, equivalent to 10 tumors in each group). Survival over time is shown as a percentage for each treatment group. P values are based on log-rank (Mantel–Cox) test
analysis (*, P < 0.05; **, < 0.005; ***, < 0.0005). QD, once daily; BID, twice daily. B, Schematic of novel NSCLC GEMM containing LSL EGFR-T790M-L858R
and LSL Trp53-R172H DN alleles (TLP mice). Mice were induced by intranasal administration at 6 weeks of age with Adenovirus-Cre recombinase. Upon
determination of lung tumor growth by MRI, mice were randomized into treatment groups and imaged biweekly until end-stage disease to determine
tumor response. C, Tumor volume index, normalized to pretreatment volume, for TLP mice treated with the indicated drugs at 2 and 4 weeks (left). Mean
± standard error of the mean is shown (*, P < 0.05; **, < 0.005; ***, < 0.0005, two-sided t test). Combination-treated mice had long-term tumor regression
(right). Tumor volume index for combination-treated mice is shown up to 14 weeks. D, Representative MRI images for mice treated with WZ4002, THZ1,
or the combination of the two, pretreatment and at week 4, showing significant tumor regression with combination treatment. Heart and tumor areas are
drawn up and marked with yellow and red lines, respectively. E, Survival curves for TLP mice treated with the indicated drugs. P value determined by
log-rank (Mantel–Cox) test analysis (*, P < 0.05; **, < 0.005; ***, < 0.0005).
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CDK7/12 Inhibition Prevents Resistance to Targeted Therapy
RESEARCH BRIEF
A
RT112 xenograft
PC9 xenograft
**
Vehicle (n = 5)
BGJ398 15 mg/kg QD (n = 5)
THZ1 10 mg/kg BID (n = 5)
BGJ398 + THZ1 (n = 5)
Survival (%)
80
**
100
60
40
Vehicle (n = 5)
Erlotinib (25 mg/kg QD) (n = 5)
THZ1 (10 mg/kg BID) (n = 5)
Erlotinib + THZ1 (n = 5)
80
Survival (%)
100
60
40
20
20
0
0
0
2
4
6
0
8
2
4
Weeks
Weeks
B
6
8
Vehicle
TLP mice
WZ4002
Ad-Cre
Biweekly MRI
End-stage disease
Tumor volume index
(normalized to pretreatment volume)
C
WZ4002 + THZ1
Tumor volume index
(normalized to pretreatment volume)
MRI to
confirm disease
LSL-EGFR-T790M-L858R;
LSL-Trp53-R172H (DN)
THZ1
TLP mice
160
140
120
100
80
60
40
20
0
−20
−40
−60
−80
−100
*
**
*
−40
−60
−80
−100
2
WZ4002
4
6
8
10
Weeks
12
THZ1
E
THZ1 + WZ4002
TLP mice
**
100
WEEK 0
14
4 weeks
Heart
Survival (%)
80
Tumor
WEEK 4
−20
Vehicle (n = 3)
WZ4002 50 mg/kg QD (n = 2)
THZ1 10 mg/kg BID (n = 2)
WZ4002 + THZ1 (n = 5)
2 weeks
D
0
60
40
Vehicle (n = 3)
WZ4002 50 mg/kg QD (n = 2)
THZ1 10 mg/kg BID (n = 2)
WZ4002 + THZ1 (n = 5)
20
0
0
2
4
6
8
10
12
14
Weeks
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RESEARCH BRIEF
combination-treated animals despite long-term treatment
(Supplementary Fig. S4d).
We further tested THZ1 in combination with crizotinib in
a previously published GEMM of EML4-ALK NSCLC (ref. 44;
Supplementary Fig. S4b and S4c). Mice in the combination
treatment arm had signiicant tumor regression compared
with crizotinib-treated mice (P = 0.013, two-tailed t test). In
this model however, mice treated with THZ1 (or combination
treatment) developed complications secondary to twice-daily
intraperitoneal injections (i.e., scarring, ascites, and peritonitis) with longer-term dosing. The increased complications
may be related to differences in the background, as C57BL/6
(TLP model) and Nu/Nu mice (xenograft studies) tolerated
THZ1 administration well compared with mice on a mixed
genetic background (EML4-ALK model).
Consistent with prior in vivo studies (15, 24–26), we show
that intraperitoneal administration of THZ1 10 mg/kg twice
daily is suficient to saturate CDK7 binding in TLP GEMM
tumor nodules and liver (Supplementary Fig. S4e).
THZ1 Impedes the Engagement of Targeted
Therapy–Induced Transcriptional Programs
that Promote Tumor Cell Survival
To investigate the mechanisms by which THZ1 may
enhance cell death and suppress resistance emergence in
combination with targeted therapy, we examined gene expression by RNA sequencing (RNA-seq) in six independent cellular models representing diverse oncogenic dependencies
and lineages (RT112, PC9, H3122, A375, N87, and A549).
Cells were treated for 48 hours with vehicle, THZ1, the corresponding targeted therapy, or combined THZ1 and targeted
therapy treatment. Treatment with targeted kinase inhibitors
induced extensive, but variable, degrees of change in gene
expression across the cellular models studied (Fig. 3A; Supplementary Fig. S5a). Consistent with prior work, targeted
therapy upregulated the expression of genes involved in
prosurvival programs, including NF-κB–, STAT-, and TGFβdriven transcription programs (refs. 12, 13, 45, 46; Supplementary Fig. S5b). Cell lines had gene expression changes
suggesting MAPK reactivation, including downregulation
of negative regulators of the MAPK pathway (i.e., DUSP,
SPRED, and SPRY family members; Supplementary Fig. S5b).
We also noted FRA1 (FOSL1) downregulation consistent
with activation of the previously described tumor secretome
(11), and upregulation of stemness factors, such as WNT/
Hedgehog and SOX family members. Downregulation of
cell-cycle genes and upregulation of cell senescence programs
further suggested transition to a quiescent cell state (Supplementary Fig. S5b). Importantly, the speciic genes altered
were generally distinct between the cell lines but highlighted
programs serving similar functions, many of which have previously been implicated in drug resistance (11–13, 31, 47–49).
The original characterization of drug-tolerant persisters (14)
was performed at later time points; thus we conirmed that
gene expression changes induced at 48 hours were stable by
comparing with gene expression following 7 days of treatment
with targeted therapy. Indeed, we found that the transcriptional programs induced and repressed at 48 hours were largely
maintained at 7 days, suggesting that this earlier time point is
relective of the drug-tolerant state (Supplementary Fig. S5c).
Changes in gene expression induced by targeted therapy
were signiicantly attenuated in the combination-treated arm,
a inding that was most prominent in RT112, PC9, and A375,
but was consistent across all six models (Fig. 3A; Supplementary Fig. S5a, S5b, and S5d). Interestingly, combination
treatment attenuated the expression of genes upregulated
by targeted therapy, as well as of those downregulated by
targeted therapy (i.e., these genes were less repressed in the
combination-treated arm). Supervised analyses further corroborated that THZ1 is interfering with the establishment of
the adaptive responses to targeted therapy (Supplementary
Fig. S5b). We noted, for instance, a diminished upregulation
of NF-κB/Interferon pathway members such as TNFSF10,
MX1, and MX2 in RT112 combination-treated cells compared with BGJ398-treated cells. Last, in contrast to the gene
expression changes noted with targeted therapy, treatment
with low-dose THZ1 alone did not alter gene expression
extensively (Fig. 3A; Supplementary Fig. S5a, S5b, and S5e).
We next considered which genes were the most perturbed
across all six models by combination treatment compared
with targeted therapy alone, and found that only 34 genes
were downregulated across multiple cell lines [three or more
Figure 3. THZ1 attenuates targeted therapy-induced transcriptional and enhancer remodeling. A, Differentially expressed genes at 48 hours following treatment with THZ1, TKI or BRAFi, or THZ1 in combination with TKI or BRAFi (Combination, C) compared with DMSO control (D), filtered by genes
that were upregulated or downregulated greater than or equal to 1.5 LFC of read counts per million with targeted kinase inhibition, or less than or equal
to −1.5 LFC, respectively. Column four shows LFC of combination-treated versus targeted therapy-treated cells (C/TKI, C/BRAFi). Heat maps show averaged values for 3 biological replicates per condition. The number of upregulated and downregulated genes (LFC ≥ 1.5 and ≤ −1.5, respectively) for each
condition is summarized below each column, in red and blue, respectively. LFC values between −1.5 and 1.5 are shown in white. B, Heat map showing the
most downregulated genes in combination-treated cells versus targeted therapy–treated cells across all six cell lines. Only genes whose expression was
downregulated less than −1.5 LFC were considered, and only genes affected in a minimum of three cell lines were included. Genes in bold are transcription factors. Genes in red are EGR1 targets. Columns on the right indicate the number of cell lines with LFC ≤ 1.25, and LFC ≤ 1.5 for each respective
gene. C, LFC of BRD4 ChIP-seq signal at superenhancer regions (SE) following 48-hour treatment with THZ1, targeted therapy (TKI, BRAFi), or THZ1
in combination with targeted therapy (C) compared with DMSO control (D). The fourth column shows combination treatment compared with targeted
therapy treatment (C/TKI). The heat map plots the union of SE regions identified in DMSO-treated cells and targeted therapy–treated cells. The number
of upregulated and downregulated regions is summarized below each column, in red and blue respectively. D, Violin plots showing the distribution of LFC
of BRD4 ChIP-seq density at superenhancer regions plotted in C, for TKI compared with DMSO control (D), combination treatment compared with targeted therapy treatment (C/TKI), and combination compared with DMSO control (C/D). E, BRD4 gene tracks for control-, THZ1-, targeted therapy (TKI)–,
and combination (Combo)-treated cells for TNFSF10 (RT112), IFIT2 (PC9), PSG5 (H3122), and JUN (A375). Signal of ChIP-seq occupancy is in reads per
million (rpm). Black bars indicate typical enhancers and red bars superenhancers. F, Immunoblot analysis for AKT and ERK activity for cells treated for
24 hours with control, the corresponding kinase inhibitor (BRAFi for A375), THZ1, or THZ1 in combination with the kinase inhibitor, at the doses used in
colony formation assays.
64 | CANCER DISCOVERY
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CDK7/12 Inhibition Prevents Resistance to Targeted Therapy
EGR1
BPIFB1
JUN
EBF4
KLF2
ATHL1
ERMAP
UBA7
TLCD2
PTGS2
ABHD11
PIGZ
DUSP1
SUSD4
ADAMTS13
NDRG2
TMEM256
TLE2
GSDMB
BTN3A3
PLA2G4A
PYROXD2
CCDC146
GPNMB
DDX60
FOS
CTSF
ORAI3
ZNF467
IFITM2
NA
HLA-F
CFB
TRPV1
45 664 110
70 516 294
2
26 564 98
71 725 170
18 147 112
4
C/BRAFi
6
6
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
8
−8
E
RT112 BRD4 ChIP-seq
rpm
C/D
BRAFi/D
THZ1/D
A375
C/TKI
TKI/D
C/TKI
THZ1/D
H3122
C/D
TKI/D
C/TKI
C/D
9
−8
LFC BRD4 ChIP-seq signal at SE
TKI/D
THZ1/D
59 900 156
148 985 474
−5
PC9
THZ1/D
RT112
C
6
−5
C/D
7
−7
168 66
LFC<− 1.25
LFC<− 1.5
RT112
PC9
H3122
A375
N87
A549
C/BRAFi
C/D
BRAFi/D
THZ1/D
C/TKI
C/D
TKI/D
C/TKI
C/D
THZ1/D
C/TKI
C/D
B
A375
LFC (gene expression)
TK1/D
THZ1/D
H3122
THZ1/D
RC9
RT112
TKI/D
A
RESEARCH BRIEF
14 DMSO
14
14
14
THZ1
TKI
Combo
chr3: 172,299,686 - 172,210,466
317 185 201
82 886 490
579 711 695
1111 307 703
1
−1
271 72
712 558 484
389 120 319
1
213 391 465
1
−1
1
−1
PC9 BRD4 ChIP-seq
rpm
TNFSF10
14
14
14
14
1
−1
DMSO
THZ1
TKI
Combo
chr10: 91,052,745 - 91,075,878
IFIT2
F
TKI
THZ1
pAKT
AKT
pERK
ERK
β-Actin/
vinculin
H3122 BRD4 ChIP-seq
rpm
1.0
1.0
0.0
0.0
−1.0
0.0
−1.0
−3
I/D /TKI C/D
C
I/D /TKI C/D
C
TK
PC9
RT112
i/D Fi C/D
AF RA
BR C/B
I
I/D /TK C/D
C
TK
TK
A375
H3122
1.0
LFC BRD4 at SE
−1 0 1 2
PC9
H3122
7
DMSO
7
THZ1
7
TKI
7
Combo
chr19: 43,696,406 - 43,644,757
PSG5
A375
−
+
−
+
−
+
−
+
−
+
−
+
−
+
−
+
−
−
+
+
−
−
+
+
−
−
+
+
−
−
+
+
A375 BRD4 ChIP-seq
rpm
RT112
−1.0
D
9
9
9
9
DMSO
THZ1
BRAFi
Combo
chr1: 59,254,081 - 59,242,081
JUN
January 2018
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RESEARCH BRIEF
lines with ≤−1.5 log2-fold change (LFC)]. This small subset
of the most perturbed genes was enriched for transcription
factors (e.g., EGR1, JUN, KLF2, FOS, and their target genes;
Fig. 3B). These indings suggest that targeted therapy may
induce a state that is dependent on the activity of these
transcription factors and is consistent with the inding that
THZ1-sensitive cell lines have overexpression of oncogenic
transcription factors and factors involved in the regulation
of RNAPII-driven transcription (15). To further investigate
the signiicance of EGR1 downregulation in the adaptive
response to targeted therapy, we generated EGR1-deicient
PC9 cells and performed colony formation assays with vehicle, erlotinib, THZ1, or combination therapy. Knockdown of
EGR1 resulted in signiicantly diminished colony formation
following erlotinib treatment compared with single-guide
(sg) dummy PC9 cells, thus partly phenocopying the effect
of THZ1 in combination with erlotinib (Supplementary Fig.
S6a and S6b). EGR1 knockdown, however, had no effect
on colony formation with THZ1 treatment alone or combination treatment. These indings further corroborate the
importance of EGR1, and EGR1-dependent programs, for the
adaptive rewiring to targeted therapies.
Considering shared upregulated genes across the six models with combination treatment versus targeted therapy alone,
we noted an enrichment for histone proteins (Supplementary
Fig. S7a). CDK7 inhibition has been shown to impair the
3′-end processing of histone mRNAs, leading to aberrant
polyadenylation (22), which may result in an artifactual gain
of histone mRNA signal in the RNA-seq analysis.
THZ1 Prevents the Rapid and Dynamic
Remodeling of the Enhancer Landscape
Elicited by Targeted Therapies
Given that tumor cells acquire enhancers and superenhancers at genes that control tumor cell identity (46–49),
and that THZ1 has been shown to disproportionally perturb
superenhancer-driven transcription (8–10), we asked whether
targeted therapy induces changes in the enhancer landscape
in our models and whether THZ1 has an impact on enhancer
remodeling. To address this we performed chromatin immunoprecipitation sequencing (ChIP-seq) for BRD4, a member
of the bromodomain and extraterminal domain family of
transcriptional coactivators and elongation factors (50). BRD4
localizes at enhancers and promoters and is known to be rapidly and dynamically regulated in response to such factors as
NF-κB pathway activation (51), and most recently in response
to inhibition by MEK1/2 in triple-negative breast cancer (52).
We found that targeted therapy led to a redistribution of
BRD4 occupancy, and the addition of THZ1 attenuated the
gain and loss of BRD4 signal elicited by targeted therapy
at superenhancers, as well as typical enhancers (Fig. 3C–E;
Supplementary Fig. S8a–S8c). In line with our transcriptome
indings, changes in BRD4 signal density induced by targeted therapy varied greatly across models (Supplementary
Fig. S8c), and these changes were largely maintained at 7
days (Supplementary Fig. S8d). Furthermore, superenhancerassociated genes had higher expression than typical enhancers, and changes in the enhancer landscape paralleled those
in gene expression (Supplementary Fig. S9a and S9b). Interestingly, THZ1 monotherapy also induced changes in BRD4
66 | CANCER DISCOVERY
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signal density that varied considerably across models (Fig. 3C;
Supplementary Fig. S8a and S8c); however, these changes did
not correspond to signiicant effects on transcription or cell
viability. Taken together, these indings suggest that THZ1
impinges on the ability of tumor cells to eficiently redistribute transcription factors and remodel enhancers that allow
them to escape targeted kinase inhibition.
Repression of the Adaptive Responses to
Targeted Therapy by THZ1 Results in More
Complete ERK Suppression
The adaptive responses to targeted therapy have previously been shown to reactivate key survival pathways, such
as AKT/PI3K and ERK/MAPK (31, 32, 34, 53, 54); therefore,
we performed a targeted immunoblotting analysis in a subset
of cellular models to consider activation of these key survival
pathways. We found that combination treatment with THZ1
and targeted therapy resulted in enhanced AKT pathway
suppression in receptor tyrosine kinase dependent models,
and more complete ERK suppression in all cell lines tested
(Fig. 3F; Supplementary Fig. S10a). These data, along with
the inding that MAPK pathway repressors, such as DUSPs,
are downregulated with targeted therapy (Supplementary
Fig. S5b), suggest that the transcriptional reprogramming
engaged by targeted therapy converges in part on MAPK
reactivation. Downregulation or deletion of MAPK negative
regulators leading to MAPK reactivation has previously been
shown to confer resistance to FGFR and ALK inhibition in
lung cancer (32, 53), BRAF inhibition in melanoma (55),
MET inhibition in gastric cancer (56), and to MEK inhibition
more broadly (57). To further consider the role of MAPK in
our models, we knocked down two well-described negative
regulators of MAPK, NF1, and SPRED2, in PC9 cells. NF1 or
SPRED2 knockdown resulted in an observable rescue from
erlotinib and to a lesser degree erlotinib plus THZ1 treatment
(Supplementary Fig. S10b–S10e). We further examined ERK
activity in NF1- or SPRED2-deicient PC9 cells following treatment with vehicle, erlotinib, THZ1, or erlotinib plus THZ1
(Supplementary Fig. S10f). NF1- and SPRED2-deicient cells
displayed greater ERK activity following erlotinib or combination treatment, compared with sg dummy PC9 cells. Combination treatment nevertheless resulted in comparatively
enhanced ERK suppression compared with erlotinib treatment alone. Cumulatively these indings suggest that ERK
activity is a key component of the cellular survival program
in the context of targeted therapy, and that repression of the
adaptive responses to targeted therapy, by THZ1, more fully
suppresses this component.
DISCUSSION
A growing body of evidence suggests that tumors are able
to evade targeted cancer therapies by an extensive repertoire
of resistance mechanisms (7, 11, 34). This poses a signiicant
therapeutic challenge, as it may not be feasible to target
the multitude of potentially relevant escape pathways in
each individual patient. Recent studies indicate that targeted therapies acutely elicit prosurvival and proproliferative
responses, which promote the persistence of a drug-tolerant
population and facilitate resistance emergence (11–14). We
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CDK7/12 Inhibition Prevents Resistance to Targeted Therapy
hypothesized that we may be able to interfere with the adaptive reprogramming response and enhance the eficacy of
targeted cancer therapy by employing a novel transcriptional
repressor, THZ1.
THZ1 is a covalent CDK7/12 inhibitor with reported singleagent activity in T-cell acute lymphoblastic leukemia (15),
MYCN-dependent neuroblastoma (24), small cell lung cancer
(25), and triple-negative breast cancer (26), and recently in
combination with BH3 mimetic drugs in peripheral T-cell
lymphomas (58). Here, we show that THZ1’s mode of action
may also be leveraged to prevent resistance emergence to targeted therapies. THZ1 in combination with targeted therapies
resulted in signiicantly enhanced cell death and decreased
resistance emergence in cellular cancer models. It led to
signiicant tumor regression and increased survival in vivo in
xenograft models and immunocompetent GEMMs. Notably,
these indings were broadly applicable across diverse oncogenic dependencies and lineages, suggesting that CDK7/12
inhibition may be a promising broad-based strategy for
enhancing the effectiveness of targeted therapies.
As noted, THZ1 is a covalent inhibitor of both CDK7 and
CDK12, and we cannot from our study conclude the extent to
which the inhibition of CDK7 or CDK12 contributes to the
noted therapeutic effect. CDK7- and CDK12-speciic compounds are in development and will aid in determining the
contribution of each to the noted synergy. Here, we show that
genetic depletion of CDK7 or CDK12 resulted in enhanced
sensitivity to erlotinib in PC9 cells, and additionally that a
novel covalent CDK12-speciic compound, THZ531 (41), as
an adjunct to targeted therapy, resulted in comparable synergy
to THZ1 in colony formation studies. These indings suggest
roles for both CDK7 and CDK12 in the noted therapeutic
synergy. CDK7-speciic compounds were not tested as none
are currently published.
Consistent with prior studies, we noted that high-dose THZ1
or genetic depletion of CDK7 or CDK12 is cytotoxic. Kwiatkowski and colleagues have previously shown that high-dose
THZ1 results in complete CDK7 inhibition, leading to global
downregulation of steady-state mRNA levels by 12 hours with
a concomitant dramatic loss of cell viability, whereas lowdose THZ1 only downregulates a subset of transcripts (15).
This subset of preferentially downregulated transcripts largely
consists of oncogenes whose high-level expression is driven by
superenhancers, as well as additional genes that form the core
regulatory circuitry of these oncogenes. In the current study,
we employed lower doses of THZ1 (∼IC50), at which transcription was modestly affected by monotherapy (Supplementary
Fig. S5e) and cell viability effects were minimal, suggesting
incomplete target inhibition. Our data suggest that this partial
CDK7/12 inhibition is suficient to hinder enhancer remodeling and the establishment of novel transcriptional circuits
elicited by targeted therapy. Thus, akin to models intrinsically sensitive to THZ1, where low-dose THZ1 disrupts the
transcription of key transcription factors thus interfering with
core preexisting oncogenic circuits, we believe that THZ1 in the
setting of targeted therapy disrupts the establishment of novel
prosurvival circuits, possibly by lowering levels of transcription
factors key to the reprogramming process.
Consistent with prior studies, we found that targeted therapies acutely induce extensive transcriptional changes that
RESEARCH BRIEF
support drug-resistance emergence (11–13, 31, 47, 48). Our
data suggest that although similar adaptive responses are
engaged across diverse models (e.g., NF-κB/STAT pathway
activation), the speciic molecules involved in the adaptive
reprogramming response vary based on the speciic cellular
context (e.g., IGFBP5 was upregulated by BGJ398 in RT112,
whereas IGFBP2 was upregulated by trametinib in A549).
These results are consistent with the extensive heterogeneity
noted in the adaptive kinome response to lapatinib in ERBB2dependent breast cancer cell lines (59) and further highlight
the advantages in preventing the adaptive reprogramming
response as a whole, rather than targeting individual tumor
type–speciic components.
We additionally found that targeted cancer therapies
elicit a rapid and dynamic remodeling of the enhancer
landscape across a spectrum of solid cancer models, supporting transcriptional programs that facilitate resistance
emergence. Acute remodeling of enhancers has been shown
to occur in response to proinlammatory stimuli, leading to
rapid inlammatory gene activation (51, 60), and recently
in response to MEK1/2 inhibition in triple-negative breast
cancer models (52). Our mechanistic studies across multiple
cellular cancer models suggest that CDK7/12 inhibition, by
THZ1, prevents active enhancer formation at genes promoting resistance emergence in response to targeted therapy and
impedes the engagement of the transcriptional programs and
signaling outputs that characterize the drug-tolerant state.
Curiously, we found that THZ1 monotherapy, at the low
doses employed in the study, had widely varying effects on
BRD4 distribution at enhancers across cellular models, yet
did not lead to signiicantly altered transcriptional outputs
or decreased cellular viability.
Our work builds conceptually on prior studies that have
suggested that blocking the adaptive reprogramming elicited
by targeted therapy may have a therapeutic impact in cancer
models. Several studies have shown that HDAC inhibitors
can suppress, in vitro, the emergence of drug resistance
(7, 14). Furthermore, HDAC inhibitors in combination with
targeted therapies have been tested in several early-phase
clinical trials in solid cancers (61–67). However, the majority
have unfortunately shown minimal, or no, response, emphasizing the need for additional strategies to counter resistance.
In addition, Stuhlmiller and colleagues (59) have shown
that BET bromodomain inhibition can suppress the transcription of lapatinib-induced kinases in ERBB2-dependent
breast cancer cell lines, preventing downstream SRC/FAK
signaling and AKT reactivation, and arresting growth
in vitro. These indings were recently extended to triplenegative breast cancer models, showing that pharmacological inhibition of BRD4 can prevent resistance emergence
to MEK inhibition (52). Further in vitro and in vivo studies
are necessary to delineate differences in the mechanism of
action of BET inhibition, compared with CDK7/12 inhibition, in the context of therapeutic synergy with targeted
therapies in diverse clinical settings.
Cancer cells that are highly dependent on transcription for
maintenance of their oncogenic state, so-called transcriptionally addicted cells, have previously been shown to be highly
susceptible to THZ1 monotherapy (15, 24, 25). Here, we
propose a model whereby targeted therapy induces a state
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RESEARCH BRIEF
of acquired transcriptional addiction, in a subpopulation of
cells poised to become drug tolerant, that is thereby highly
vulnerable to THZ1 treatment. This provides a potentially
powerful therapeutic intervention that obviates the need to
anticipate or elucidate the myriad of drug resistance mechanisms that might arise in a particular patient.
METHODS
Cell Lines
RT112, PC9, NCI-H3122, N87, OE19, NCI-H2077, NCI-H1975,
HCC827, EBC-1, NCI-H1703, A549, NCI-H23, NCI-H1792, and GSU
cells were cultured in RPMI media, and A375 and NCI-H2009 cells were
cultured in DMEM. Both types of media were supplemented with 10%
FBS and penicillin/streptomycin/L-glutamine. All cell lines were cultured
at 37°C in a humidiied chamber in the presence of 5% CO2. Cell lines
were obtained from ATCC, Sigma-Aldrich, or collaborating labs primarily
in 2014. A375, N87, EBC-1, GSU, and NCI-H2009 were obtained in 2009.
Cell lines were not authenticated. Cells were not passaged for more than
6 months. Cell lines used for RNA-seq/ChIP-seq studies and for animal
studies were Mycoplasma tested and negative (MycoAlert PLUS, Lonza).
Cell Viability Assays
Cells (1,500 per well) were seeded in 96-well plates, allowed to
adhere overnight, and then incubated with media containing vehicle
or drug as indicated for 96 hours. Following 96 hours, cell viability
was assessed using the CellTiter-Glo Luminescent Cell Viability
assay (Promega). Plates were read on a Tecan Ininite M200 Pro
plate reader. All conditions were tested in triplicate, unless otherwise
noted. Drug curves and IC50 values were generated using GraphPad
Prism 6 (GraphPad Software).
Colony Formation Assays
Cells (100,000 per well) were seeded in 6-well plates, allowed to
adhere overnight, and then incubated with media containing vehicle
or drug as indicated for 4 weeks, unless otherwise noted. Media (and
drug) were replaced weekly. At 4 weeks, wells were washed twice with
PBS and ixed with 1% paraformaldehyde for 15 minutes at room
temperature. Wells were then washed again with PBS twice and stained
with 0.1% crystal violet solution for 15 minutes at room temperature.
Lastly, wells were gently washed with deionized water and allowed
to dry overnight. Control wells were stained by 1 week. Results were
quantiied using an ImageJ Colony Area PlugIn (68). Values of less than
1% on the quantiication were considered as not detectable (ND). Drug
doses were as follows: RT112: BGJ398 1 µmol/L, THZ1 100 nmol/L
(Fig. 1) and 150 nmol/L (Supplementary Fig. 2F and G); PC9: erlotinib
1 µmol/L, THZ1 100 nmol/L; NCI-H3122: crizotinib 250 nmol/L,
THZ1 50 nmol/L; A549: trametinib 200 nmol/L, THZ1 150 nmol/L;
NCI-H23: trametinib 500 nmol/L, THZ1 100 nmol/L; NCI-H1792:
trametinib 500 nmol/L, THZ1 500 nmol/L; NCI-H2077: BGJ398 1
µmol/L, THZ1 10 nmol/L; NCI-H1975: WZ4002 1 µmol/L, THZ1
500 nmol/L; HCC827: erlotinib 50 nmol/L, THZ1 75 nmol/L; N87:
lapatinib 100 nmol/L, THZ1 25 nmol/L; OE19: lapatinib 150 nmol/L,
THZ1 125 nmol/L; EBC-1: crizotinib 10 nmol/L, THZ1 50 nmol/L;
NCI-H1703: imatinib 1 µmol/L, THZ1 30 nmol/L; GSU: trametinib 10
nmol/L, THZ1 200 nmol/L; NCI-H2009: trametinib 50 nmol/L, THZ1
25 nmol/L; A375: vemurafenib 1 µmol/L, THZ1 50 nmol/L.
Apoptosis/Cell Death Analysis
Cells (100,000 per well) were seeded in 6-well plates, allowed to
adhere overnight, and then incubated with media containing vehicle
or drug as indicated for 24 hours or 48 hours. Cell death was quantiied using the Alexa Fluor 488 Annexin V/Dead Cell Apoptosis kit
for low cytometry (Invitrogen), according to the manufacturer’s
68 | CANCER DISCOVERY
January 2018
protocol. All conditions were assayed in triplicate. Data were acquired
using a BD LSRFortessa X-20 (BD Biosciences), and analyzed in FlowJo.
Xenograft Tumor Studies
Xenograft studies were approved by the Dana-Farber Cancer Institute Animal Care and Use Committee. RT112, PC9, and A549 xenograft models were established by subcutaneous (s.c.) injection of 2 ×
106 cells in Matrigel (Corning) into both lanks of nude mice (NU/
NU, #088 Charles River) when animals were 8 to 10 weeks of age.
The xenograft studies were powered to include 5 mice (10 tumors per
treatment group) providing 82% power to detect an underlying difference in survival between 70% and 10% at 8 weeks in Fisher’s exact
test at a one-sided 0.05 level. When tumors reached between 100 and
200 mm3, as measured by caliper, mice were randomized to four
groups of ive female mice each, for each cell line: (i) vehicle,
(ii) BGJ398 (RT112), erlotinib (PC9) or trametinib (A549), (iii) THZ1,
or (iv) combination treatment with THZ1 plus BGJ398 (RT112),
erlotinib (PC9), or trametinib (A549). The animals were randomized
to treatment using simple randomization by cage. Investigators were
not blinded to group allocation. The following dosing regimens were
employed: BGJ398 15 mg/kg once daily by oral gavage, erlotinib
25 mg/kg once daily by oral gavage, trametinib 2.5 mg/kg once daily
by oral gavage, and THZ1 10 mg/kg twice daily by intraperitoneal
(i.p.) injection. BGJ398 was dissolved in PEG300, erlotinib was dissolved in 0.5% methylcellulose and 0.4% Tween 80, and THZ1 was
dissolved in 10% DMSO in D5W. Caliper measurements were then
performed weekly and continued for 8 weeks. A549 xenografts had
severe ulcerations and therefore were excluded from the study. RT112
xenografts had one mouse in the combination-treated group that was
censored at week 5 (found dead, cause not known, tumor size small).
PC9 xenografts had one mouse in the erlotinib-treated arm censored
at week 1 due to ulceration.
Genetically Engineered EGFR-p53-Mutant
NSCLC Mouse Model
The study was approved by the Dana-Farber Cancer Institute
Animal Care and Use Committee. Mice (both male and female) bred
to contain the conditional EGFR-T790M-L858R lox-stop-lox (LSL)
allele and the Trp53-DN R172H LSL allele to a inal genotype of
EGFR-T790M-L858RLSL/−; Trp53-R172HLSL/− maintained on a mixed
background were induced at 6 weeks of age with Adenovirus Cre
recombinase by intranasal administration (69) to allow for Cremediated recombination of LSL modiied mutant-EGFR and p53
alleles. Upon clinical signs of disease, MRI was performed to establish
pretreatment tumor burden in the lungs (generally 16–20 weeks of
age). Mice were imaged using a 7 Tesla BioSpec (Bruker Biospin) optimized for image requisition of pulmonary parenchyma and vessels in
mice. Animals were anesthetized with 2% isolurane IsoFlo; Abbott)
in 100% oxygen via a nose cone. Respiratory and cardiac gating was
applied to minimize motion artifacts during imaging. Twenty-four
slices (1 mm) were collected. Tumor volume per animal was quantiied manually, based on a minimum of eight consecutive axial image
sequences, using the 3D Slicer. Upon determination of the pretreatment volume, mice were randomized (by simple randomization) into
treatment groups as follows: (i) vehicle, (ii) WZ4002 (covalent T790Mmutant-EGFR selective inhibitor, 50 mg/kg once daily by oral gavage), (iii) THZ1 (10 mg/kg, twice daily, i.p.), or (iv) THZ1 + WZ4002.
WZ4002 was dissolved in 5% N-methylpyrrolidone, and THZ1 in
10% DMSO in D5W. Pharmacokinetics properties of THZ1 are provided in Kwiatkowski and colleagues (15) and Wang and colleagues
(26). Investigators were not blinded to group allocation. Mice were
imaged biweekly by MRI until end-stage disease to determine tumor
volume. Mice weights and signs of toxicity were monitored daily
during the course of treatment. End-stage disease was reached when
animals acquired clinical symptoms secondary to their lung tumors,
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Published OnlineFirst October 20, 2017; DOI: 10.1158/2159-8290.CD-17-0461
CDK7/12 Inhibition Prevents Resistance to Targeted Therapy
in accordance with Dana-Farber Cancer Institute Animal Care and
Use Committee regulations.
Genetically Engineered EML4-ALK NSCLC Mouse Model
This study was approved by the Dana-Farber Cancer Institute
Animal Care and Use Committee. A previously described GEMM
of NSCLC with doxycycline-inducible EML4-ALK was employed
(70). Upon determination of the pretreatment volume by MRI (as
described above for the TLP GEMM), mice were randomized into
treatment groups as follows: (i) vehicle, (ii) crizotinib (50 mg/kg once
daily by oral gavage), (iii) THZ1 (10 mg/kg, twice daily, i.p.), or (iv)
THZ1+crizotinib. Investigators were not blinded to group allocation.
Crizotinib was dissolved in 5% hydroxypropyl methylcellulose, and
THZ1 in 10% DMSO in D5W. Mice were imaged at 2 and 4 weeks by
MRI to determine tumor volume. Mice weights and signs of toxicity were monitored daily during the course of treatment. End-stage
disease was reached when animals acquired clinical symptoms secondary to their lung tumors, in accordance with Dana-Farber Cancer
Institute Animal Care and Use Committee regulations.
RNA-seq Analysis
RNA was isolated from RT112, PC9, H3122, A375, N87, and A549
following treatment with DMSO, the appropriate targeted therapy,
THZ1, or targeted therapy in combination with THZ1 at the doses
used in colony formation assays (see above relevant section for dosing). Cells were harvested following 48 hours of treatment. For the
targeted therapy arm, cells were also harvested following 7 days of
treatment. Cell number was determined and total RNA was isolated
using the RNeasy micro kit (Qiagen). Ambion ERCC RNA Spike-In
Mix (Life Technologies) was added to total RNA. cDNA libraries were
prepared using the NEBNext Ultra RNA Library Prep Kit for Illumina
(New England BioLabs) according to the manufacturer’s instructions. Library integrity was assessed on an Agilent 2100 Bioanalyzer
(Agilent). Sequencing was performed on the HiSeq 2000 platform
(Illumina) to a minimum depth of 30 million reads per sample.
Quality control–passed reads were aligned to the human reference
genome (hg19) using bowtie2 (71) and quantiied as gene-level read
counts using RSEM (72). Genes assigned less than ive reads in all
samples were removed. For each cell line analyzed at a given time point
and conditions (untreated, THZ1-treated, targeted therapy–treated,
and combination-treated), read count data were post-processed by
removing low-expressed genes, applying data normalization, and calculating differential expression. Only genes expressing over ten reads
in at least three samples were retained. Read counts were normalized
to log-counts-per-million values with the voom transformation (73).
Expression changes for each gene in treated cells compared with
untreated controls was determined using the limma package (74) as
log2-transformed fold change and a multiple-testing adjusted P value.
Heat map visualization was performed using R. Log2-transformed
fold changes were not scaled and were colored on a blue–red scale.
EGR1 target genes were deined based on JASPAR Predicted
Transcription Factor Targets (ref. 75; http://amp.pharm.mssm.edu/
Harmonizome/dataset/JASPAR+Predicted+Transcription+Factor+
Targets).
ChIP Sample Preparation
RT112, PC9, H3122, A375, N87, and A549 cells were treated
for 48 hours with vehicle, targeted therapy, THZ1, and targeted
therapy in combination with THZ1 at the doses employed in colony formation assays (see above relevant section for dosing). Cells
were cross-linked for 10 minutes at room temperature by the addition of one-tenth of the volume of 11% formaldehyde solution
(11% formaldehyde, 50 mmol/L HEPES pH 7.5, 100 mmol/L NaCl,
1 mmol/L EDTA pH 8.0, 0.5 mmol/L EGTA pH 8.0) to the growth
media followed by 5 minutes quenching with 2.5 mol/L glycine.
RESEARCH BRIEF
Cells were washed twice with PBS, the supernatant was aspirated and
cells collected, and the cell pellet was lash-frozen in liquid nitrogen.
Frozen cross-linked cells were stored at −80°C. Dynal magnetic beads
(50 µL; Sigma) were blocked with 0.5% BSA (w/v) in PBS. Magnetic
beads were bound with 10 µg of the indicated antibody. For BRD4
occupied regions, we performed ChIP-seq experiments using a Bethyl
antibody (cat# A301-985A100). For H3K27Ac occupied regions, we
performed ChIP-seq experiments using an Abcam antibody (cat#
AB4729, lot# GR183922-1). Cross-linked cells were lysed with lysis
buffer 1 (50 mmol/L HEPES pH 7.5, 140 mmol/L NaCl, 1 mmol/L
EDTA, 10% glycerol, 0.5% NP-40, and 0.25% Triton X-100), pelleted
and resuspended in lysis buffer 2 (10 mmol/L Tris–HCl pH 8.0, 200
mmol/L NaCl, 1 mmol/L EDTA, 0.5 mmol/L EGTA). The subsequent
pellet was resuspended in and sonicated in sonication buffer (50
mmol/L HEPES pH 7.5, 140 mmol/L NaCl, 1 mmol/L EDTA pH 8.0,
1 mmol/L EGTA, 0.1% Na-deoxycholate, 0.1% SDS, and 1% Triton
X-100). Lysates were sonicated for 4 minutes (1-second ‘ON’ and
4-seconds ‘OFF’) at 40% amplitude on a QSonica Sonicator on ice.
Sonicated lysates were cleared and incubated overnight at 4°C with
magnetic beads bound with antibody to enrich for DNA fragments
bound by the indicated factor. Beads were washed two times with
sonication buffer, one time with sonication buffer with 500 mmol/L
NaCl, one time with LiCl wash buffer (10 mmol/L TrisHCl pH 8.0,
1 mmol/L EDTA, 250 mmol/L LiCl, 0.5% NP-40, 0.5% Na-deoxycholate) and one time with TE buffer. DNA was eluted in elution buffer
(50 mmol/L TrisHCl pH 8.0, 10 mmol/L EDTA, 1% SDS). Cross-links
were reversed overnight. RNA and protein were digested using RNase
A and Proteinase K, respectively, and DNA was puriied with phenol
chloroform extraction and ethanol precipitation.
ChIP-seq Analysis
Illumina sequencing libraries were generated and data was processed
according to ref. 76. In brief, libraries were generated following the Illumina TruSeqTM DNA Sample Preparation v2 kit protocol with minor
changes. All ChIP-seq data sets were aligned using bowtie to build
NCBI36/hg19 of the human genome with -p 4 –best -k 2 -m 2 –sam
-l 40. Wiggle iles for gene tracks were created using MACS (77) 1.4.2
with options –w –S –space=50 to count reads in 50bp bins. These were
divided by the number of treatment reads to normalize to mappedreads-per-million, and were displayed in the UCSC genome browser.
Superenhancers were identiied using BRD4 ChIP-seq and the
ROSE algorithm (https://bitbucket.org/young_computation/rose/).
Enhancer constituents were identiied using MACS with input control
and with two parameter sets: –keep-dup=1 –p 1e-9 and –keep-dup=all
–p 1e-9. The collapsed union of these regions was used as input for
ROSE with parameters –s 12,500 –t 1,000 and input control. Superenhancers identiied in the DMSO and targeted-therapy conditions
were collapsed to capture both baseline and acquired superenhancers
upon targeted-therapy treatment. The list of combined DMSO and
targeted-therapy superenhancers were associated with expressed genes
by inding the single expressed Ensembl transcript whose transcription start site was nearest the center of the superenhancer.
Densities of H3K27Ac or BRD4 ChIP-seq reads (Fig. 3C and D;
Supplementary Figs. S7 and S8) were calculated using bamToGFF
(https://github.com/BradnerLab/pipeline). Superenhancers identiied in DMSO or targeted therapy conditions described above were
treated as one bin (-m 1), reads were extended to be 200bp (default)
and the reads-per-million (-r) normalized density (-d) of reads was
calculated therein. These RPM-normalized density values were log2
normalized after addition of one pseudocount, and log2 values were
used for fold-change calculations.
Data Deposition
RNA-seq and ChIP-seq data have been submitted to the NCBI
Gene Expression Omnibus under accession number GSE89129.
January 2018
CANCER DISCOVERY | 69
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Published OnlineFirst October 20, 2017; DOI: 10.1158/2159-8290.CD-17-0461
Rusan et al.
RESEARCH BRIEF
Immunoblotting
Cells were lysed in RIPA buffer (Roche) containing protease
inhibitors (Roche) and Phosphatase Inhibitor Cocktails I and II
(CalBioChem). Protein concentrations were determined using a
Bradford assay (Bio-Rad). Proteins were separated by SDS gel electrophoresis using NuPAGE 4% to 12% Bis–Tris gels (Life Technologies)
in MOPS buffer. Resolved protein was transferred to nitrocellulose
membranes, blocked in 10% milk and probed with primary antibodies recognizing AKT (9272S), pAKT (4060P), ERK (4695S), pERK
(4370S), CDK12 (11973), tubulin (3873S), EGR1 (4153S; all from
Cell Signaling Technology), CDK7 (sc-723, Santa Cruz Biotechnology), SPRED2 (ab153700, Abcam), NF1 (sc-67, Santa Cruz Biotechnology), actin (A5441, Sigma-Aldrich) and vinculin (V9131,
Sigma-Aldrich) in 5% milk or 3% bovine serum albumin as recommended by the manufacturer. After incubation with the appropriate
secondary antibody [Pierce anti-mouse IgG/IgM (31444, Thermo
Scientiic) and anti-rabbit IgG (31460, Thermo Scientiic)], blots were
imaged on ilm.
Pulldown Experiments
TLP mice were treated for 72 hours with vehicle or THZ1 (10 mg/
kg twice daily, i.p.), and livers and tumors were harvested 6 hours following the last treatment. Tumor and liver tissues were homogenized
and lysed as previously described (25) and incubated with bio-THZ1
for pulldown as previously described (15). CDK7 target engagement
was analyzed by immunoblotting.
CRISPR/Cas
CDK7/CDK12 Experiments. Target sequences for CRISPR interference were designed using the sgRNA designer (http://www.broa
dinstitute.org/rnai/public/analysis-tools/sgrna-design) and CRISPR
Design tool (http://crispr.mit.edu), provided by the Broad Institute,
MIT, and Feng Zhang lab, MIT, respectively. Off-target effects were
considered using http://www.genome-engineering.org. A nontargeting sgRNA from the Gecko library v2 was used as a dummy sgRNA
for control (78).
Sequences were as follows:
dummy guide 5′ATCGTTTCGCTTAACGGCG3′;
CDK7 sgRNA#1 5′TGTGATGCAAAGGTATTCCA3′;
CDK7 sgRNA#2 5′ATACACATCAGGTTGTAACC3′;
CDK7 sgRNA#3 5′TGAGAAGCTGGACTTCCTTG3′;
CDK12 sgRNA#1 5′GCTTGTGCTTCGATACCAAG3′;
CDK12 sgRNA #2 5′GCTCCCAGACTGGAATTAAG3′;
CDK12 sgRNA #3 5′GTAGGAGTCATAATTGCTCG3′.
Lenti CRISPRv2 vectors were cloned as previously described (78,
79). Briely, HEK-293T cells were transduced with lentiCRISPRv2
using X-treme Gene 9 (Roche) according to the manufacturer’s
instructions. On day 2, PC9 cells were seeded, and allowed to adhere
overnight. On day 3, the supernatant of transduced HEK293T cells
was collected and added to the PC9 cells through a 0.45 µm ilter.
Supernatant from transduced HEK293T cells was again collected
and added to PC9 cells on day 4. On day 5, puromycin (1 mg/mL) was
added to select infected cells (for four days).
NF1/SPRED2/EGR1 Experiments. Oligonucleotides coding for
guide RNAs that target the NF1 and SPRED2 genes were chosen from
the Avana library (80) and cloned into lentiGuide-Puro two-vector
system using established methods (78). The sequence for the oligonucleotides are as follows (dummy guide was as above):
NF1_sg1 5′ GATATATCCAAAGACG 3′;
NF1_sg2 5′GGTGGAATGGGTCCAGGCCG3′;
NF1_sg3 5′ TCTTTAGTCGCATTTCTACC3′;
SPRED2_sg1 5′ACCAGAGATGACTCCAGCGG3′;
70 | CANCER DISCOVERY
January 2018
SPRED2_sg2 5′ AGGTTGCTCTCTCTTCTGAG3′;
SPRED2_sg3 5′CAAAGGCTCGGGCATCAGCA3′;
EGR1_sg1 5′CGGCCAGTATAGGTGATGGG3′
EGR1_sg2 5′AAGGCCTTAATAGTAGACAG3′;
EGR1_sg3 5′GAGTGAGGAAAGGATCCGAA3′.
RT-PCR
Total cellular RNA was isolated from cells using an RNeasy Mini
Kit (Qiagen) and 1.0 µg was then reverse transcribed to cDNA using
the High Capacity RNA to c-DNA kit (Life Technologies). Quantitative PCR reactions were performed on an ABI Prism 7300 platform
(Life Technologies). CDK7 expression was checked using the following forward primer: 5′-GGGACAGTTTGCCACCGTTT-3′ and
reverse primer: 5′-ATGTCCAAAAGCATCAAGGAGAC-3′. CDK12
expression was checked using the following forward primer: 5′-GAG
GAGGCAGCAGAGAAGAG-3′ and reverse primer: 5′-TAAAAGTT
GCAGCAAGGCGG-3′. CDK7 and CDK12 primers were designed using
Primer 3 software. Relative gene expression was normalized to human
GAPDH using the following forward primer: 5′-TTAGGAAAGCCT
GCCGGTGACTAA-3′ and reverse primer: 5′-AAAGCATCACCCGGA
GGAGAAATC-3′ (81).
Statistical Analysis
Data are expressed as mean ± standard deviation. Statistical signiicance was determined using Student t test, Mann–Whitney U test, or
one-way ANOVA. For survival analyses, log-rank test (Mantel–Cox)
was used. Statistical analyses were performed in Prism 6 (GraphPad
Software). Signiicance was set at P = 0.05.
Disclosure of Potential Conflicts of Interest
B.J. Abraham has ownership interest (including patents) in Syros
Pharmaceuticals. N. Kwiatkowski has ownership interest in DFCI
patents licensed to Syros Pharmaceuticals. M. Meyerson reports
receiving a commercial research grant from Bayer, has ownership
interest (including patents) in LabCorp, and is a consultant/advisory
board member for Origimed. N.S. Gray has ownership interest
(including patents) in Syros and is a consultant/advisory board
member for the same. R.A. Young has ownership interest (including patents) in Syros Pharmaceuticals and is a consultant/advisory
board for the same. No potential conlicts of interest were disclosed
by the other authors.
Authors’ Contributions
Conception and design: M. Rusan, K. Li, N. Kwiatkowski, N.S. Gray,
P.S. Hammerman
Development of methodology: M. Rusan, K. Li, Y. Li, B. Bockorny,
T. Chen, L. Tan, T. Shimamura, P.S. Hammerman
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): M. Rusan, K. Li, C.L.
Christensen, N. Kwiatkowski, K.A. Buczkowski, B. Bockorny, S. Li,
K. Rhee, H. Zhang, W. Chen, H. Terai, T. Tavares, A.L. Leggett, S.-H.
Hong, N. Poudel-Neupane, T. Shimamura, N.S. Gray, K.-K. Wong
Analysis and interpretation of data (e.g., statistical analysis,
biostatistics, computational analysis): M. Rusan, K. Li, Y. Li,
C.L. Christensen, B.J. Abraham, N. Kwiatkowski, K.A. Buczkowski,
B. Bockorny, T. Chen, K. Rhee, W. Chen, H. Watanabe, N.S. Gray,
R.A. Young, K.-K. Wong, P.S. Hammerman
Writing, review, and/or revision of the manuscript: M. Rusan,
K. Li, Y. Li, C.L. Christensen, B.J. Abraham, N. Kwiatkowski, K.A.
Buczkowski, B. Bockorny, S. Li, W. Chen, A.L. Leggett, T. Li, Y. Wang,
M. Meyerson, A.J. Bass, N.S. Gray, K.-K. Wong, P.S. Hammerman
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): B. Bockorny,
H. Zhang, W. Chen, T.-J. Kim, N. Poudel-Neupane, T. Mudianto, L. Tan,
T. Shimamura, K.-K. Wong, P.S. Hammerman
www.aacrjournals.org
Downloaded from cancerdiscovery.aacrjournals.org on March 12, 2018. © 2018 American Association for Cancer Research.
Published OnlineFirst October 20, 2017; DOI: 10.1158/2159-8290.CD-17-0461
CDK7/12 Inhibition Prevents Resistance to Targeted Therapy
Study supervision: M. Rusan, T. Chen, M. Meyerson, R.A. Young,
K.-K. Wong, P.S. Hammerman
Other (discovered and provided CDK7 inhibitor): T. Zhang
Other (helped conduct research especially in vivo portion):
M. Silkes
The costs of publication of this article were defrayed in part by
the payment of page charges. This article must therefore be hereby
marked advertisement in accordance with 18 U.S.C. Section 1734
solely to indicate this fact.
Received May 1, 2017; revised October 2, 2017; accepted October
17, 2017; published OnlineFirst October 20, 2017.
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