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Targeting MYC dependency in ovarian cancer through inhibition of CDK7 and CDK12/13

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Cite this article as: eLife 2018;7:e39030 doi: 10.7554/eLife.39030

Abstract

High-grade serous ovarian cancer is characterized by extensive copy number alterations, among which the amplification of MYC oncogene occurs in nearly half of tumors. We demonstrate that ovarian cancer cells highly depend on MYC for maintaining their oncogenic growth, indicating MYC as a therapeutic target for this difficult-to-treat malignancy. However, targeting MYC directly has proven difficult. We screen small molecules targeting transcriptional and epigenetic regulation, and find that THZ1 - a chemical inhibiting CDK7, CDK12, and CDK13 - markedly downregulates MYC. Notably, abolishing MYC expression cannot be achieved by targeting CDK7 alone, but requires the combined inhibition of CDK7, CDK12, and CDK13. In 11 patient-derived xenografts models derived from heavily pre-treated ovarian cancer patients, administration of THZ1 induces significant tumor growth inhibition with concurrent abrogation of MYC expression. Our study indicates that targeting these transcriptional CDKs with agents such as THZ1 may be an effective approach for MYC-dependent ovarian malignancies.

https://doi.org/10.7554/eLife.39030.001

Introduction

Epithelial ovarian cancer (OC) is the fifth most common cause of female cancer death in the United States and the most lethal gynecologic malignancy (Siegel et al., 2017). High-grade serous ovarian carcinoma (HGSOC) represents the most common and aggressive histologic subtype of OC, and accounts for the majority of its deaths (Konstantinopoulos and Awtrey, 2012). Large-scale genomic studies have demonstrated that HGSOCs are characterized by high degree of genomic instability with high frequency of DNA copy number alterations and almost universal presence of TP53 mutations (Cancer Genome Atlas Research Network, 2011). Approximately 50% of HGSOCs exhibit an underlying defect in DNA repair via homologous recombination (HR) and are highly sensitive to double-strand-break-inducing agents such as platinum analogues and PARP-inhibitors (PARPi) (Konstantinopoulos et al., 2015). However, although first-line platinum-based chemotherapy results in clinically complete remissions in approximately 70% of OC patients, relapse occurs in more than 90% of these patients, at which point the disease is much less responsive to subsequent treatment and is essentially non-curable. Similarly, despite initial responses to PARPi among HR-deficient HGSOCs, acquired resistance occurs commonly and represents a significant barrier to the long-term survival of these patients (Lord and Ashworth, 2017). Overall, the outlook for patients with platinum and PARPi-resistant disease is poor, so novel therapeutic strategies are urgently needed (Vaughan et al., 2011).

The control of gene transcription involves a set of cyclin-dependent kinases (CDKs), including CDK7, CDK8, CDK9, CDK11, CDK12, CDK13, and CDK19, that play essential roles in transcription initiation and elongation by phosphorylating RNA polymerase II (RNAPII) and other components of the transcription apparatus (Larochelle et al., 2007; Zhou et al., 2012). Recent studies have shown that certain oncogenes, for example MYC, MYCN, and RUNX1 exhibit significant dependence on continuous active transcription, and that inhibition of the general transcriptional machinery may allow for highly selective effects on these oncogenes in cancer cells before global downregulation of transcription occurs (Kwiatkowski et al., 2014Cao and Shilatifard, 2014; Chipumuro et al., 2014). The continuous active transcription of these oncogenes in cancer cells is often driven by exceptionally large clustered enhancer regions, termed super-enhancers, which are densely occupied by transcription factors and co-factors (Hnisz et al., 2013; Lovén et al., 2013). In this vein, it was recently shown that CDK7 mediates transcriptional addiction to a vital cluster of genes associated with super-enhancers in triple-negative breast cancer (TNBC), and that TNBC cells are exceptionally dependent on CDK7 (Wang et al., 2015). The CDK7 covalent inhibitor THZ1, which also inhibits the closely related kinases CDK12 and CDK13 (CDK12/13), has been also shown to directly suppress super-enhancer-associated oncogenic transcription in T-cell acute lymphoblastic leukemia, neuroblastoma and small cell lung cancer (Kwiatkowski et al., 2014Chipumuro et al., 2014; Christensen et al., 2014).

Here, we identified THZ1 as a highly potent compound that downregulates MYC expression. THZ1 demonstrates exceptional in vivo activity in patient-derived xenograft (PDX) models of ovarian cancer that were platinum and PARPi resistant. Notably, suppression of MYC was only achieved by simultaneous inhibition of CDK7, CDK12, and CDK13. Our data suggest that combined inhibition of transcriptional CDKs with THZ1, or its derivatives, may be an effective approach for treating MYC-dependent ovarian cancer.

Results and discussion

MYC is frequently amplified in ovarian cancer and is essential for cancer cell growth

Previous large-scale studies of HGSOC demonstrated extensive copy number alterations (Cancer Genome Atlas Research Network, 2011). Among the total eight recurrent chromosome-arm gains, chromosome 8q has the most significant gains and occurred in 65% of the tumors (n = 489) (Cancer Genome Atlas Research Network, 2011). Analyzing the updated TCGA dataset that includes more patient samples also indicate the widespread 8q gain, in addition to 8 p loss (Figure 1A).

Figure 1 with 1 supplement see all
MYC is frequently amplified in ovarian cancer and required for cancer cell growth.

(A) Copy number plots of TCGA high-grade serous ovarian cancer samples for chromosome 8 (top) and part of the q24 arm (bottom). Red color indicates a high chromosomal copy number ratio, blue represents low (see color key on the right). Data were analyzed and plotted using UCSC Xena Functional Genomics Browser (xena.ucsc.edu). (B) Frequency of MYC amplification across cancer types. (C) Correlation between MYC copy number and its gene expression in ovarian cancer. The relative copy number value and normalized RNA-seq expression values of MYC were downloaded from cBioportal and plotted in GraphPad Prism. Pearson correlation coefficient was measured and the p-value<1×10−4. (D) CRISPR/Cas9-mediated gene editing in ovarian cancer cells. Immunoblotting of lysates from ovarian cancer cells that were infected with lentivirus encoding Cas9 and sgRNA targeting GFP or MYC, and then harvested 2 days after puromycin selection (top). Cells were fixed after 12 days and stained with crystal violet (bottom). (E) Effect of JQ1 in ovarian cancer cells (top) and in a multiple myeloma line MM1.S (bottom). Cells were treated with vehicle control (DMSO) or increasing concentrations of JQ1 for 6 hr before lysates were prepared for immunoblotting with the indicated antibodies. Also see Figure 1—figure supplement 1.

https://doi.org/10.7554/eLife.39030.002

Inspired by earlier investigations of ovarian cancer reporting the amplification of 8q regions as well as that of MYC oncogene in 8q24 (Baker et al., 1990; Etemadmoghadam et al., 2009; Staebler et al., 2006), we focus on the amplification of MYC in ovarian cancer. Notably, ovarian cancer demonstrates the highest frequency of MYC amplification (Figure 1B), compared to many other tumor types. We further analyzed and found a significant correlation between the gene copy number of MYC and its gene expression level (assayed by RNA sequencing) (Figure 1C). Therefore, MYC has widespread amplification in ovarian cancer, and its amplification typically correlates with high-level expression of the MYC oncogene.

Driven by the extensive alterations on both MYC gene copy and expression scales, we next proceeded to evaluate the functional role of MYC in OC lines. We utilized CRISPR/Cas9-mediated gene editing technique to disrupt the expression of MYC (Sanjana et al., 2014), and observed an efficient loss of MYC protein in cells infected with lenti-virus encoding two independent MYC-targeting guide RNAs (Figure 1D, top). MYC-depleted KURAMOCHI and OVCAR8 cells showed a significant deficit in cell viability compared to control cells as determined by clonogenic growth survival assay (Figure 1D, bottom). In addition, we analyzed the large-scale CRISPR screen performed by Broad Institute, a study where they developed the CERES computational model to reduce the false-positive differential dependencies caused by multiple DNA breaks resulted from targeting amplified regions (Meyers et al., 2017). In the analysis illustrating MYC dependency in a total of 484 cancer cell lines, there is no statistical correlation between MYC dependency values and with MYC copy number (Figure 1—figure supplement 1A), indicating the successful computational elimination of effects introduced by targeting amplified MYC. Notably, ovarian cancer cells overall demonstrate a high dependence on MYC (indicated by the low CERES values), and as expected, MYC dependency is highly correlated with cell dependency on MAX – a partner of MYC for transcriptional regulation (Figure 1—figure supplement 1B). These data further confirm the functional roles of MYC for ovarian cancer cell proliferation and indicate MYC as a promising therapeutic target for ovarian cancer.

Screening transcriptional/epigenetic regulators identifies THZ1 as a potent inhibitor for MYC expression in ovarian cancer cells

Given the prominent gene amplification/overexpression of MYC and its critical roles for ovarian cancer cell growth, we next explored pharmacologic strategies for targeting MYC. Since the MYC protein lacks characteristics enabling specific and direct binding to small molecule compounds, recent studies have focused on approaches to interrupt the genesis of MYC transcript/protein or key downstream functions of MYC. In this regard, several studies have shown that inhibition of BET bromodomain proteins can effectively downregulate MYC transcription and consequently the growth of MYC-dependent cancer cells (Delmore et al., 2011; Mertz et al., 2011). Consistent with previous studies (Delmore et al., 2011), we found that MYC expression in the multiple myeloma line MM1.S is highly sensitive to JQ1; treating cells with JQ1 at nanomolar concentrations was sufficient to downregulate MYC protein abundance (Figure 1E, bottom). Surprisingly, this effect was not observed in ovarian cancer cells (Figure 1E, top), a phenotype reminiscent of that seen in other non-hematologic cancer cells, such as lung adenocarcinoma cells (Lockwood et al., 2012), and some triple-negative breast cancer cells (Shu et al., 2016).

To identify regulatory pathways that control MYC gene transcription in ovarian cancer, we proceeded to screen a selected group of chemicals. We selected 42 compounds, derived from the HMS small molecule library (http://lincs.hms.harvard.edu/db/sm/), targeting various transcriptional and/or epigenetic components, such as histone modification enzymes, transcriptional CDKs, transcriptional co-activators, and DNA modification enzymes (Figure 2A; Supplementary file 1). The compounds were used for treating two OC cell lines, KURAMOCHI and COV362. Whole cell lysates were then harvested for fluorescent immunoblotting, and MYC protein signal was normalized to that of loading control in the same membrane (Figure 2A; Figure 2—figure supplement 1). In both cell lines, these inhibitors display various effects on MYC protein abundance, and the most potent inhibitor is THZ1, an inhibitor known to target CDKs 7, 12, and 13 (Kwiatkowski et al., 2014) (Figure 2BFigure 2—figure supplement 1; Supplementary file 2).

Figure 2 with 2 supplements see all
Targeted screen of transcriptional and epigenetic regulators identifies THZ1 as key transcriptional regulator of MYC.

(A) Schematic diagram of a screen for small molecules that inhibit MYC expression. Ovarian cancer cells were treated with selected epigenetic/transcriptional inhibitors for 6 hr at a final concentration of 1 μM. Whole cell lysates were subjected to fluorescent immunoblotting with anti-MYC and anti-α-tubulin (loading control) antibodies. (B) Normalized signals of MYC by immunoblotting in KURAMOCHI cells. THZ1 was the most potent inhibitor that reduces MYC expression. (C) The indicated cells were treated with increasing concentrations of THZ1 for 6 hr. Cell lysates were subjected to immunoblotting using the indicated antibodies. (D) qPCR analysis of MYC and MCL-1 in THZ1-treated ovarian cancer cells. Student’s t-test was performed and data were presented as mean values ± SD of technical triplicates. Also see Figure 2—figure supplement 1; Figure 2—figure supplement 2; Supplementary file 1 and Supplementary file 2.

https://doi.org/10.7554/eLife.39030.004

We next validated the potency of THZ1 for inhibiting MYC expression in ovarian cancer cells. MYC protein abundance was decreased by THZ1 at ~250 nM and was further abolished when higher doses of THZ1 were used (Figure 2C). Consistent with the roles of CDK7 in regulating the phosphorylation of the carboxyl-terminal domain (CTD) of RNA polymerase II subunit RPB1, CTD phosphorylation at Ser2, 5, and 7 was suppressed by THZ1(Figure 2C).

MCL-1 expression is sensitive to THZ1

Interestingly, the anti-apoptotic protein of Bcl-2 family, pro-survival protein myeloid cell leukemia 1 (MCL-1), was repressed by THZ1 concomitantly (Figure 2C; Figure 2—figure supplement 1A–B). We further examined the mRNA level of MYC and MCL-1, and found that treating both KURAMOCHI and COV362 cells with THZ1 (250 nM) led to a 50% or greater reduction of both transcripts (Figure 2D), indicating that the transcriptional inhibition of MYC and MCL-1 genes may be largely responsible for the observed reduction in protein abundance.

Although less prevalent than MYC amplification, MCL-1 amplification was observed in 12% of HGSOCs of the TCGA dataset. Notably, in the TCGA, HGSOCs that harbored both MYC and MCL-1 amplification were associated with poor outcome, which was significantly worse compared to all remaining HGSOCs (Figure 2—figure supplement 2A, left panel). Additionally, among MYC amplified HGSOCs, those that co-exhibited MCL-1 amplification had significantly worse prognosis compared to MYC amplified tumors without MCL-1 amplification (Figure 2—figure supplement 2A, right panel), indicating a role of MCL-1 during disease progression.

Interestingly, out of the total eight ovarian cancer cell lines (KURAMOCHI, COV362, OVCAR8, SKOV3, IGROV1, OVSAHO, OVCAR3, OVCAR4), the only cell line (SKOV3) that is least sensitive to THZ1 in terms of cell growth inhibition demonstrates low protein abundance of both MYC and MCL-1 (Figure 2—figure supplement 2B–C). While THZ1-sensitive lines, such as KURAMOCHI and OVACAR8, are susceptible to CRISPR/Cas9-mediated gene editing of MYC (Figure 1D) or MCL-1 (Figure 2—figure supplement 2D), the THZ1-insensitive line SKOV3 was largely unaffected by the loss of MYC or MCL-1 (Figure 2—figure supplement 2E). These data suggest that the expression levels of MYC and MCL-1 in OC cells may determine its sensitivity to THZ1.

THZ1 represses MYC target genes in ovarian cancer cells

We next performed global gene expression profiling in ovarian cancer cells to investigate the effects of THZ1 on transcriptional programs. In two THZ1-sensitive cell lines KURAMOCHI (Ku) and COV362 (Cov), we treated cells for 6 hr with vehicle control or THZ1 at two concentrations (50 and 250 nM) in duplicates, and then subjected samples for RNA isolation, library construction, and sequencing.

We analyzed the actively expressed genes (n = 14,068) in two ovarian cancer cell lines and the global change of gene expression was displayed in a concentration-dependent manner upon THZ1 treatment, shown by the heatmaps (Figure 3A). A profound suppression of gene expression was observed following treatment of THZ1 at 250 nM in both cell lines, with 1060 genes downregulated and 58 genes upregulated in Ku cells (Figure 3B, top), and 1524 genes downregulated and 50 genes upregulated in Cov cells (Figure 3B), bottom). Among these, both MYC and MCL-1 were significantly downregulated in both cell lines, with the cutoff of Log2 fold change >0.5 and FDR adjusted p-value<0.001 (Figure 3B). The regulation on MYC expression by THZ1 appears quite specific, because PVT1 − a lncRNA gene co-amplified with MYC on 8q24 − is not significantly downregulated by THZ1 (Figure 3—figure supplement 1).

Figure 3 with 2 supplements see all
THZ1 represses MYC target genes in ovarian cancer cells.

(A) Heatmaps showing row normalized gene expression for all actively expressed genes (n = 14,068) (cell count normalized FPKM > 1 in at least one sample). (B) Scatter plots comparing change in gene expression upon THZ1 treatment to gene expression levels in DMSO. X-axis shows the Log2 fold change of expression (THZ1/DMSO). Y-axis shows the adjusted log2 counts per million (CPM) expression in DMSO. Differentially regulated genes as determined by edgeR are shown for downregulated (blue) and upregulated (red) genes. A FDR adjusted p-value cutoff of 1e-3 and a log fold change (LFC) cutoff of 0.5 are used as the threshold for significance. (C) Left - Venn diagram showing the intersection of differential downregulated genes between Ku and Cov cells upon THZ1 250 nM treatment. Significance of overlap is determined by a fisher's exact test. p-value and odds ratio are shown. Right - Scatter plots comparing the log2 fold change in gene expression upon THZ1 250 nM treatment between Ku and Cov cells. Differential downregulated genes that are shared between Ku and Cov are shown in blue. The pearson correlation of differential genes is shown (Rho = 0.76). (D) Bar plot showing gene sets from the MSigDB Hallmarks of Cancer that are significantly enriched (FDR q value < 1e-3) amongst the 685 genes shared as differentially downregulated between Ku and Cov upon 250 nM THZ1 treatment. MYC target gene sets are shown in red. (E) Box plots showing the log2 fold change in gene expression upon THZ1 treatment for MYC target genes (red, n = 58) and other active genes (grey, n = 14,010) in Ku and Cov cell lines. MYC target genes are drawn from the HALLMARKS_MYC_TARGETS_V2 signature. The statistical significance between MYC target genes and other active genes is shown from a Wilcoxson rank sum test (one-sided). Also see Figure 3—figure supplement 1; Figure 3—figure supplement 2.

https://doi.org/10.7554/eLife.39030.007

Next, we examined how many downregulated genes were shared between Ku and Cov cells and what the potential pathways these genes might be involved in. There were 685 differential downregulated genes been identified upon THZ1 250 nM treatment, shown by the Venn diagram (p-value for significance of overlap <2e-16) (Figure 3C, Left) and by the scatter plot(Figure 3C, Right), with the overlapped downregulated genes shown in blue (Pearson correlation coefficient Rho = 0.76). To search for the oncogenic pathways that were selectively downregulated by THZ1, we queried the Molecular Signature Database (MSigDB) Hallmarks of Cancer in Gene Set Enrichment Analysis (Broad Institute). G2M_checkpoint and E2F_targets appeared to be the top two hallmark gene sets that were significantly enriched amongst the 685 genes in Ku and Cov upon THZ1 250 nM treatment. Two MYC hallmark gene sets, MYC_targets_V1 and MYC_targets_V2 (shown in red), were also identified (Figure 3D). Additionally, these two MYC hallmark gene sets were also significantly enriched when analyzed individually (Figure 3—figure supplement 2). When comparing the MYC target genes (defined from the HALLMARKS_MYC_TARGETS_V2 gene set, in red) with all other active genes (in gray), they were significantly downregulated upon THZ1 250 nM treatment in both cell lines (Figure 3E). This analysis is consistent with THZ1 downregulating a MYC-dependent gene expression program.

MYC and MCL-1 downregulation require the inhibition of both CDK7 and CDK12/13

Given the polypharmacology of THZ1, we asked whether the loss of MYC and MCL-1 resulted from inhibition of CDK7, or CDK12/13, or combined inhibition of CDK7 and CDK12/13. To do this, we turned to two recently described compounds that were selective for CDK7 (YKL-1–116, Kalan et al., 2017) or CDK12/13 (THZ531, Zhang et al., 2016) (Figure 4A). Compared with THZ1 (Figure 2C), YKL-1–116 exhibited similar but less potent effects on MYC downregulation in two OC cell lines (Figure 4B–C, Figure 4—figure supplement 1A–B). Different from THZ1 and YKL-1–116, the CDK12/13 inhibitor, THZ531, has a unique effect on MYC expression: at low doses of THZ531 MYC expression is induced, while higher doses of THZ531 cause MYC to be repressed (Figure 4B–C, Figure 4—figure supplement 1A–B). To test whether the combined CDK7 and CDK12/13 inhibition enhances MYC and MCL-1 downregulation, we treated cells with the combination of YKL-1–116 and THZ531. Interestingly, we found that the combination of YKL-1–116 and THZ531 led to pronounced reduction of MYC and MCL-1, recapitulating the effects of THZ1 (Figure 4D–E, Figure 4—figure supplement 1C–D). In addition, the combination of YKL-1–116 and THZ531 further reduced the phosphorylation of RNA polymerase II RPB1 at Ser5, but not at Ser 2 and 7 (Figure 4E and Figure 4—figure supplement 1D). Although it remains to be understood how MYC and MCL-1 transcription requires the activity of both CDK7 and CDK12/13, our data indicate that the ability of THZ1 to downregulate MYC and MCL-1 is likely due to its multi-targeting effect on CDK7 and CDK12/13.

Figure 4 with 2 supplements see all
Downregulation of MYC and MCL-1 requires co-inhibition of CDK7 and CDK12/13 in KURAMOCHI cells.

(A) Chemical structures of inhibitors and their primary protein targets. (B) KURAMOCHI cells were treated with increasing concentrations of YKL-1–116 or THZ531, with cell lysates prepared following 6 hr of treatment. (C) qPCR analysis of MYC and MCL-1 in YKL-1–116 or THZ531-treated KURAMOCHI cells for 6 hr. Student’s t-test was performed and data were presented as mean values ± SD of technical triplicates. (D) qPCR analysis of MYC and MCL-1 in single or combination treatment with YKL-1–116 and THZ531 for 6 hr. Red arrows indicate an enhanced repression by the combination treatment. Student’s t-test was performed and data were presented as mean values ± SD of technical triplicates.(E) Combining YKL-1–116 and THZ531 efficiently downregulates MYC expression. KURAMOCHI cells were treated with increasing concentrations of YKL-1–116 (0.5 or 1 μM), in combination with increasing concentrations of THZ531 (0.0625, 0.125, 0.25, or 0.5 μM) for 6 hr before lysates were prepared for immunoblotting. Also see Figure 4—figure supplement 1; Figure 4—figure supplement 2.

https://doi.org/10.7554/eLife.39030.010

We next proceeded to investigate whether downregulation of MYC by THZ1 was mediated through CDK7 inhibition, we overexpressed HA-tagged wild-type CDK7 (WT) or C312S mutant (CS) in ovarian cancer cells by lentiviral infection. Cysteine 312 is the site for covalent modification, and its mutation to serine prevented THZ1 from covalently binding to CDK7 and from inhibiting CDK7 activity in an irreversible manner (Kwiatkowski et al., 2014). First, we examined whether downregulation of MYC by THZ1 can be rescued by CDK7 the C312S mutant. Expectedly, we find that the mutant CDK7 (C312S), but not the wild type, effectively rescues THZ1-induced MYC downregulation upon THZ1 treatment at 250 and 500 nM for 6 hr (Figure 4—figure supplement 2A). Concurrently, other readouts of CDK7 activity, such as CTD phosphorylation of RNAPII at Ser 5 and Ser 7, are also rescued by this CDK7 mutant (Figure 4—figure supplement 2A). These data indicate that CDK7 is indeed a target of THZ1 in downregulating MYC. Although overexpression of mutant CDK7 (C312S) fails to rescue cell growth inhibition conferred by THZ1 (Figure 4—figure supplement 2B), it significantly rescues cell growth inhibition by the selective CDK7 inhibitor, YKL-1–116 (Figure 4—figure supplement 2B–2C). We would expect that a greater degree of rescue might require the elimination of endogenous CDK7. In addition, considering that THZ1 targets both CDK7 and CDK12/13, we speculate that co-overexpression of mutant CDK7 (C312S), mutant CDK12 (C1039S), and mutant CDK13 may rescue cell growth inhibition by THZ1. Unfortunately, we failed to achieve overexpression of CDK12/13, likely due to their large sizes (1490 aa for CDK12, and 1512 aa for CDK13). Notably, expression of a THZ1-resistant mutant of CDK7 rescued THZ1-induced MYC downregulation but was not sufficient to rescue the aberrant cancer cell growth. This indicates that parallel pathways to MYC may also be altered by THZ1 and may contribute to the observed phenotypes upon THZ1 treatment.

THZ1 suppresses the growth of patient-derived ovarian tumors

Next, we evaluated the therapeutic efficacy of THZ1 in ovarian tumor models. Although THZ1 targets expression of more genes than MYC via CDK7/12/13 inhibition, its high efficiency in downregulating MYC and other genes that are critical for ovarian cancer cells, such as MCL-1, provides strong rationale for translational development. We used the orthotopic ovarian patient-derived xenografts (PDX) models that we established previously (Liu et al., 2017). The primary tumor cells were transduced with luciferase gene to enable the use of bioluminescent imaging for measurement of tumor growth.

To compare the single agent potency of THZ1 with combination effect of drugs, we performed a combination study with THZ1 and PARP inhibitor Olaparib, a FDA-approved drug in relapsed ovarian cancer irrespective of BRCA1/2 status. We first conducted tolerability studies and found that THZ1 administered by intraperitoneal injection (IP) twice daily (BID) at 10 mg/kg was well-tolerated with no signs of overt toxicity as judged by body weight and animal behavior (data not shown). In efficacy studies, we first implanted ascites-derived ovarian tumor cells into the mice, and after 7 days assigned animals into four groups receiving vehicle control (10 ml/kg, PO, QD) or THZ1 (10 mg/kg, IP, BID) or Olaparib (100 mg/kg, PO, QD) or combo (THZ1 +Olaparib) for 27 days, with bioluminescent imaging performed at 5 timepoints (0, 6, 13, 20, and 27 days) (Figure 5A–B). Consistent with previous studies of THZ1 or Olaparib, mouse body weight was minimally affected by the inhibitor (Figure 5—figure supplement 1). In all the 11 independent PDX models investigated, the administration of THZ1 caused significant inhibition on tumor cell growth (Figure 5B–C). Notably, in four models (DF-149, 172, 83, and 86), THZ1 induced complete inhibition on tumor growth (Figure 4C, termed category i). In six models (DF-101, 106, 118, 20, 68, and 216), THZ1 first caused an obvious decrease of tumor burden but re-gained growth at later time points (termed category ii). Only one model (DF-181, termed category iii) did not demonstrate tumor regression and rather present slower tumor cell growth upon THZ1 treatment. The administration of Olaparib did not dramatically inhibit tumor growth, and only showed very modest effect in three models (DF-106, 68, and 83). The combination of THZ1 and Olaparib, however, displayed synergistic effect and further inhibition on tumor growth was observed in five models (DF-106, 118, 86, 181, and 68).In addition, we found that the protein abundance of both MYC and MCL-1 in the tumor was nearly abrogated following THZ1 treatment (Figure 5D). Overall, the potency of THZ1 in suppressing tumor growth in our ovarian tumor models is striking, given that tumor regression is rarely observed in previous studies using THZ1. The combination study indicated that combining THZ1 with clinical PARP inhibitors could be promising future therapeutic approach for treating ovarian cancer.

Figure 5 with 1 supplement see all
THZ1 abrogates the growth of patient-derived high-grade serous ovarian cancer cells in vivo.

(A) A schematic diagram of the experimental design. (B) Luminescence picture of luciferized PDX mice (eg. DF-106) at day 0 and day 27, treated with vehicle control (10 ml/kg, PO, QD) or THZ1 (10 mg/kg, IP, BID) or Olaparib (100 mg/kg, PO, QD) or Combo (THZ1 +Olaparib)., Luminescence scale bar was shown on the right. (C) Luminescence signal of the tumor in 11 PDX models (n = 44 mice) treated with vehicle control or THZ1 or Olaparib or Combo. (D) Following the last treatment on day 27, tumor ascites from 5 PDX models (n = 10 mice) were harvested for the preparation of whole cell lysates followed by immunoblotting using the indicated antibodies. Also see Figure 5—figure supplement 1.

https://doi.org/10.7554/eLife.39030.013

In summary, our study demonstrates an exceptional antitumor activity of THZ1 in models of ovarian cancer and point to at least part of its mechanistic actions whereby an oncogenic transcription factor with widespread alteration in ovarian cancer, MYC, is strongly suppressed by this compound. Thus the current study provides a compelling rationale for investigating THZ1, or its derivatives, for treating MYC-dependent ovarian cancer in the clinic. In addition, we find that suppression of MYC expression can only be achieved by simultaneous inhibition of CDK7, CDK12 and CDK13, further demonstrating the advantage of polypharmacology in overcoming functional redundancy in transcriptional regulation through targeting multiple CDKs.

Materials and methods

Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
AntibodyRabbit monoclonal anti-c-MYC (Y69)ABCAMCat# ab32072; RRID: AB_7316581:1000 dilution
AntibodyRabbit polyclonal anti-MCL-1Santa Cruz BiotechnologyCat# sc-819; RRID: AB_21441051:1000 dilution
AntibodyMouse monoclonal anti-alpha-TubulinCell Signaling TechnologyCat# 3873S; RRID: AB_19041781:5000 dilution
AntibodyRabbit monoclonal anti-HA tagCell Signaling TechnologyCat# 3724; RRID: AB_15495851:1000 dilution
AntibodyRabbit polyclonal anti-GAPDHCell Signaling TechnologyCat# 2118; RRID: AB_5610531:3000 dilution
AntibodyRabbit polyclonal anti-RNA Polymerase II RPB1Bethyl LaboratoriesCat# A300-653A; RRID: 5193341:500 dilution
AntibodyRat monoclonal anti-RNA Polymerase II subunit B1 (phosphor CTD ser-2)EMD MilliporeCat# 04–1571; RRID: AB_26874501:1000 dilution
AntibodyRat monoclonal anti-RNA Polymerase II subunit B1 (phosphor CTD ser-5)EMD MilliporeCat# 04–1572; RRID: AB_ 26874511:5000 dilution
AntibodyRat monoclonal anti-RNA Polymerase II subunit B1 (phosphor CTD ser-7)EMD MilliporeCat# 04–1570; RRID: AB_26874521:5000 dilution
Peptide,
recombinant protein
THZ1Synthesized in our laboratory
Peptide, recombinant proteinTHZ531Synthesized in our laboratory
Peptide, recombinant proteinYKL-1–116Synthesized in our laboratory
Commercial assay or kitCellTiter-Glo Luminescent Cell Viability AssayPromegaCat# G7573
Commercial assay or kitRNeasy Plus Mini kitQiagenCat# 74136
Commercial assay or kitHigh-Capacity RNA-to-cDNA KitApplied BiosystemsCat# 4387406

Commercial assay or kit

SYBR
Select Master Mix
Applied BiosystemsCat# 4472908
Cell line (Homo sapiens)KURAMOCHIPanagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL 1345
Cell line (Homo sapiens)COV362Panagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_2420
Cell line (Homo sapiens)OVCAR8Panagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_1629
Cell line (Homo sapiens)IGROV1Panagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_1304
Cell line (Homo sapiens)SKOV3Panagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_0532
Cell line (Homo sapiens)CAOV3Panagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_0201
Cell line (Homo sapiens)OVCAR3Panagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_0465
Cell line (Homo sapiens)OVCAR4Panagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_1627
Cell line (Homo sapiens)OVSAHOPanagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_3114
Cell line (Homo sapiens)PEO1Panagiotis A.
Konstantinopoulos’s laboratory
RRID:CVCL_Y032
Cell line (Homo sapiens)JHOS2Panagiotis A. Konstantinopoulos’s laboratoryRRID:CVCL_4647
Cell line (Homo sapiens)293TJean Zhao’s laboratory
Cell line (Homo sapiens)MM1.SJames Bradner's laboratory
Recombinant DNA reagentpLenti_crispr_sgGFPThis paperPlasmid
Recombinant DNA reagentpLenti_crispr_sgMYC_1This paperPlasmid
Recombinant DNA reagentpLenti_crispr_sgMYC_2This paperPlasmid
Recombinant DNA reagentpLenti_crispr_sgMCL1_1This paperPlasmid
Recombinant DNA reagentpLenti_crispr_sgMCL1_2This paperPlasmid
Recombinant DNA reagentpTrex_HA_CDK7 WTThis paperPlasmid
Recombinant DNA reagentpTrex_HA_CDK7 C312SThis paperPlasmid
Software, algorithmGraphPad PrismGraphpad Software Inchttps://www.graphpad.com/scientific-software/prism/
Software, algorithmImage Studio LiteLI-COR Bioscienceshttps://www.licor.com/bio/products/software/image_studio_lite/
Software, algorithmImageJNational Institutes of Healthhttps://imagej.nih.gov/ij/
Software, algorithmVector NTIInvitrogenhttps://www.thermofisher.com/us/en/home/life-science/cloning/vector-nti-software.html

Cell culture

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All cells were grown in RPMI1640 or DMEM medium (Life Technologies), supplemented with 10% fetal bovine serum (Gibco), 50 units/mL penicillin, 50 units/mL streptomycin, and maintained in humidified 37°C/5%CO2 incubator. Ovarian cancer cell lines, including KURAMOCHI, COV362, IGROV1, OVCAR8, OVSAHO, SKOV3, CAOV3, JHOS2, PEO1, OVCAR3 and OVCAR4, were generous gifts from Panos Konstantinopoulos’s laboratory at Dana-Farber Cancer Institute. All the cell lines have been tested to be mycoplasma-free using MycoAlert Mycoplasma Detection Kit (Lonza).

Immunoblotting

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Cells were washed once with 1x phosphate buffered saline (PBS) and then lysed in RIPA buffer (50 mM Tris, pH 7.5, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, and 0.1% SDS) supplemented with protease and phosphatase inhibitors (Roche). Protein concentrations were determined by using the Pierce BCA protein assay kit (Life Technologies). Equal amount of protein was resolved on SDS-PAGE and was subsequently transferred onto nitrocellulose membrane (Bio-Rad). The membrane was blocked with Odyssey block buffer TBS (LI-COR Biosciences) and was then incubated with primary antibodies in 20% of Odyssey block buffer TBST (with 0.1%Tween20) overnight at 4°C with gentle rotating. After washing, the membrane was incubated with fluorophore-conjugated secondary antibodies (1: 10,000) in 20% of Odyssey block buffer TBST (with 0.1% Tween20) for 1 hr at room temperature. The membrane was then washed three times in TBST and scanned with an Odyssey Infrared scanner (Li-Cor Biosciences). Primary antibodies include anti-alpha-Tubulin (Cell Signaling Technology # 3873S), anti-c-MYC (Y69) (ABCAM # ab32072), anti-MCL-1(S-19) (Santa Cruz # sc-819), anti-RNA polymerase II (Bethyl Laboratories #A300-653A), anti-RNA polymerase II subunit B1 (phospho CTD Ser-2, clone 3E10) (Millipore #04–1571), anti-RNA polymerase II subunit B1 (phospho-CTD Ser-5, clone 3E8) (Millipore #04–1572), anti-RNA polymerase II subunit B1 (phospho-CTD Ser-7, clone 4E12) (Millipore #04–1570), GAPDH (Cell Signaling Technology #2118), and anti-HA-tag (C29F4) (Cell Signaling Technology #3724). Secondary antibodies include IRDye 800CW Goat anti-Rabbit IgG (LICOR Biosciences #926–32211), IRDye 680LT Goat anti-Mouse IgG (LICOR Biosciences #926–68020), and IRDye 680RD Goat anti-Rat IgG (LICOR Biosciences #925–68076).

Plasmids and primers

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Plasmids: pLenti_CRISPR_v2 vector was ordered from Addgene (Addgene #52961).

pLenti_sgGFP, pLenti_sgMYC_1, pLenti_sgMYC_2, pLenti_sgMCL1_1, pLenti_sgMCL1_2, pTrex_HA_cdk7 WT, and pTrex_HA_cdk7 C312S lentiviral plasmids were made in our laboratory.

Primers for cloning sgRNA into pLenti_CRISPR_v2 vector

sgGFP_1F: CACCGGGGCGAGGAGCTGTTCACCG

sgGFP_1R: AAACCGGTGAACAGCTCCTCGCCCC

sgMYC_1F: CACCGAACGTTGAGGGGCATCGTCG

sgMYC_1R: AAACCGACGATGCCCCTCAACGTTC

sgMYC_2F: CACCGGCCGTATTTCTACTGCGACG

sgMYC_2R: AAACCGTCGCAGTAGAAATACGGCC

sgMCL1_1F: CACCGGCTTCCGCCAATCACCGCGC

sgMCL1_1R: AAACGCGCGGTGATTGGCGGAAGCC

sgMCL1_2F: CACCGCTCGGCCCGGCGAGAGATAG

sgMCL1_2R: AAACCTATCTCTCGCCGGGCCGAGC

Primers for qPCR:

GAPDH_qPCR_forward: GGTCTCCTCTGACTTCAACA

GAPDH_qPCR_reverse: GTGAGGGTCTCTCTCTTCCT

MYC_qPCR_forward: GGCTCCTGGCAAAAGGTCA

MYC_qPCR_reverse: CTGCGTAGTTGTGCTGATGT

MCL1_qPCR_forward: TGCTTCGGAAACTGGACATCA

MCL1_qPCR_reverse: TAGCCACAAAGGCACCAAAAG

Virus infection

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Lentiviruses were generated in HEK293T cells by transfecting cells with packaging DNA plus pLenti_CRISPR plasmids. Typically, 2 μg pLenti_CRISPR plasmid encoding sgRNA, 1.5 μg pCMVdR8.91, and 0.5 μg pMD2-VSVG, 12 μl Lipofectamin2000 (Invitrogen) were used. DNA and lipid were pre-diluted in 300 μl Opti-MEM (Invitrogen) individually and then mixed well gently. After 30 min of incubation at RT, the DNA-lipid mixtures were added dropwise to HEK293T cells (2 × 106 cells were seeded in one T-25 flask, one day prior to transfection). Viral supernatant was collected 2 and 3 days after transfection, filtered through 0.45 μm membranes, and added to target cells in the presence of polybrene (8 μg/ml, Millipore). Target cells were infected twice with the virus at 48 hr and 72 hr later. Puromycin (1 μg/ml for KURAMOCHI and OVCAR8; 2 μg/ml for SKOV3) was used to treat cells for 2 days for selection, which eliminated all cells in an uninfected control group. Cells were harvested 4 days after the initial viral infection and subjected them for either western blotting to assess the knockdown efficiency or for clonogenic cell growth assay. For lentiviral infection with pTrex_HA_cdk7 WT and pTrex_HA_cdk7 C312S plasmids, cells were infected as described above, and were selected with G418 (1 mg/ml) for 5 days then treated with 0.2 μg/ml Doxycycline for 1 day to induce the HA tagged-CDK7 overexpression. Doxycycline was replenished every 3 days during the following assays.

Cell proliferation assays

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After virus infection and selection with puromycin, cells were seeded in 12-well plates (at the density of 5 × 103) in 1 ml medium. 14 days later, cells were fixed with 1% formaldehyde for 15 minutes, and stained with crystal violet (0.05%, wt/vol), a chromatin-binding cytochemical stain for 15 minutes. The plates were washed extensively in plenty of deionized water, dried upsidedown on filter paper, and imaged with Epson scanner.

For the 3-day cell proliferation assay in 96-well plate, cells were plated at the density of 6000 to 10,000 cells per well and treated with THZ1 or YKL-1–116 of various concentrations on the next day. After 72 hr incubation, CellTiter-Glo reagent (Promega #G7572) was added to cells directly and luminescent signal was read on a plate reader (Perkin Elmer EnVision).

RNA extraction

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Cells were plated in six-well plates and allowed to adhere overnight. Following treatment with compounds for 6 hr, cells were lysed, homogenized using QIAshredder spin column (Qiagen #79654), and subjected to total RNA extraction using RNeasy Plus Mini kit (Qiagen #74136). This kit contains gDNA columns to remove genomic DNA, according to the manufacturer’s instructions.

RT-qPCR

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First strand cDNA was synthesized from 2 µg of total RNA using The High Capacity cDNA Reverse Transcription Kit (Life technologies #4368814). The cDNAs were diluted 15-fold in deionized water and then mixed with specific primers (10 μM) and 2X SYBR Select Master Mix (Applied Biosystems #4472908). The reactions were set up in MicroAmp Fast Optical 96-Well Reaction Plate (Life Technologies #4346906) and were run on 7500 Real-Time PCR System (Applied Biosystems). The GAPDH gene was used as a housekeeping gene control. Relative gene expression was calculated using the comparative method (2^–ΔΔCt).

RNA-sequencing

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Following total RNA extraction, cell-count-normalized RNA samples were mixed with the RNA standards - External RNA Control Consortium (ERCC) Spike-In Mix (Ambion, 4456740) prior to library construction (Lovén et al., 2012). Libraries were prepared using TruSeq Stranded mRNA Library Prep Kit (Illumina), and equimolar libraries were multiplexed and sequenced on an Illumina NextSeq 500 (single end 75 bp reads) by the Molecular Biology Core Facility at the Dana-Farber Cancer Institute. Fastq files were aligned to human genome build hg19 using HiSat with default parameters. Transcripts were assembled, and Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values were generated using cuffquant and cuffnorm from the cufflinks pipeline (Trapnell et al., 2010). FPKM values were then normalized to synthetic ERCC spike-in RNAs as described previously (Lovén et al., 2012). A transcript was considered to be expressed in each data set if in at least one experimental condition the normalized FPKM >1.

Animal studies

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All animal experiments were conducted in accordance with the animal use guidelines from the NIH and with protocols (Protocol # 11–044) approved by the Dana-Farber Cancer Institute Animal Care and Use Committee. We used the ovarian patient-derived xenografts (PDX) models that we established previously (Liu et al., 2017). The primary tumor cells were transduced with luciferase gene to enable the use of non-invasive bioluminescent imaging (BLI) for measurement of tumor growth. Briefly, ovarian cancer cells were taken from consented patients with HGSOC and implanted intraperitoneally into immunocompromised NOD-SCID IL2Rγnull mice (NSG, Jackson Laboratory). 5 × 106 ascites-derived cells were implanted in each mouse and 7 days post implantation, mice were imaged by BLI and assigned to four groups of treatment with vehicle control via oral gavage (PO) once daily (QD) at the dose of 10 ml/kg; THZ1 intraperitoneally (IP) twice daily (BID) at the dose of 10 mg/kg; Olaparib via PO, QD at the dose of 100 mg/kg; the combination of THZ1 and Olaparib. Tumor growth was assessed every 7 days (0, 6, 13, 20, 27 days) using BLI until day 27. Upon harvesting, tumors or other tissues were snap-frozen in liquid nitrogen for preparation of lysates and immunoblotting.

Data availability

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RNA-sequencing data reported in this paper has been deposited to the NCBI GEO and are available under the accession number GSE116282.

References

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Decision letter

  1. Charles L Sawyers
    Senior Editor; Memorial Sloan-Kettering Cancer Center, United States
  2. Ross L Levine
    Reviewing Editor; Memorial Sloan-Kettering Cancer Center, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Targeting MYC dependency in ovarian cancer through inhibition of CDK7 and CDK12/13" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Charles Sawyers as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This is a relatively straightforward study that proposes that transcriptional downregulation of MYC and possibly MCL1 through inhibition of CDKs7, 12 and 13 is a potential therapeutic avenue for MYC amplified or overexpressed ovarian cancer. While the authors have conducted a nice series of experiments, the conclusions are overly biased towards effects on MYC and MCL1 and disregard other transcriptional effects of THZ1 that are evident from the study. Moreover, the authors must do a better job of acknowledging the potential contribution of off-targets (i.e. proteins other than CDK7, CDK12, or CDK13) in this study and they need further experiments to verify whether CDK7 and/or CDK12/13 are in fact the relevant targets. These issues are particularly important given many of the conclusions have been made in previous papers, with a difference here being a focus on ovarian cancer.

Essential revisions:

1) The lack of clear mechanistic insight into how THZ1 inhibits MYC expression and target expression in MYC amplified tumors is a relative shortcoming. How do the authors believe that this agent can reduce the expression of a gene which is on a recurrent amplicon? Are there other genes on 8q whose expression is increased and how are they affected by the drug? Specifically, the data presented clearly shows that PVT1 is also amplified and increased in its expression, which has been demonstrated to independently contribute to ovarian cancer pathogenesis. Is PVT1 a transcriptional target of THZ1/JQ1 in these cells? In addition, CDK7 inhibition by YKL-1-116 alone was able to decrease mRNA levels of MYC, whereas THZ-531 (targeting CDK12 and CDK13) was not. This effect appears to be cell type-specific, because the results in Figure 3 (KURAMOCHI line) were different and somewhat more consistent with the statement in the Abstract. Pursuant to this, the authors have not conclusively demonstrated that CDK7, CDK12, or CDK13 are the relevant targets in this study. At minimum, the authors should test point mutants of CDK7 and/or CDK12 and CDK13 to confirm that they are the relevant targets in these cell lines.

2) The relative impact of the drug on MYC and MCL-1 vs. downstream targets is not clear. Can this be delineated further? Genetic rescue of MYC and MCL1 should also be performed to provide more than correlative data. Moreover, the effects observed of the MYC targeting sgRNAs is attributed to the loss of MYC expression, however, a major problem with targeting sgRNAs to amplified regions in the genome is the induction of DNA damage and cell death, independent of the sgRNA target (Aguirre AJ et al., 2017, Cancer Discovery). Are these cells responsive to shRNA mediated depletion of MYC or can the effect be rescued by MYC overexpression?

3) The PDX data is the most exciting. One wonders if there are agents used in R/R ovarian cancer which can be tested in combination to determine if the first studies should be monotherapy or combination studies.

https://doi.org/10.7554/eLife.39030.022

Author response

Essential revisions:

1) The lack of clear mechanistic insight into how THZ1 inhibits MYC expression and target expression in MYC amplified tumors is a relative shortcoming. How do the authors believe that this agent can reduce the expression of a gene which is on a recurrent amplicon? Are there other genes on 8q whose expression is increased and how are they affected by the drug? Specifically, the data presented clearly shows that PVT1 is also amplified and increased in its expression, which has been demonstrated to independently contribute to ovarian cancer pathogenesis. Is PVT1 a transcriptional target of THZ1/JQ1 in these cells?

This is an important question. Our study includes several lines of evidence that CDK7 inhibitors produce highly potent and on-targeting inhibition in suppressing MYC expression. For example, treating a variety of ovarian cancer cell lines with THZ1 for brief periods (4 – 6 hours) at nanomolar concentrations (50-250 nM) can nearly abolish the protein expression of MYC, as well as its transcript (as measured by qPCR or RNA-seq) (Figure 2C-D; Figure 3B). We further demonstrate that the robust effects on MYC expression can be fully rescued by expressing THZ1-resistant form of CDK7 (see below response to point #2). All these observations strongly suggest that CDK7 inhibitors can suppress MYC expression in MYC-amplified ovarian cancer cells. The reviewer poses an intriguing question regarding whether amplified genes are subject to transcriptional inhibition. Although such a topic has not been extensively studied, we know from literature that a number of amplified oncogenes are susceptible to transcriptional inhibition, such as targeting MYCN in neuroblastoma cells (Chipumuro et al., 2014). Similarly, BET inhibitors were found to suppress MYC expression in leukemia cell lines with MYC amplification (Mertz et al., 2011).

The regulation of MYC expression by THZ1 appears quite specific, because PVT1 − a lncRNA gene co-amplified with MYC on 8q24 − is not significantly downregulated by THZ1 (see Figure 3—figure supplement 1).

In addition, CDK7 inhibition by YKL-1-116 alone was able to decrease mRNA levels of MYC, whereas THZ-531 (targeting CDK12 and CDK13) was not. This effect appears to be cell type-specific, because the results in Figure 3 (KURAMOCHI line) were different and somewhat more consistent with the statement in the Abstract.

Please see Author response image 1 (adapted from manuscript Figure 4B; Figure 4—figure supplement 1A). DK7 inhibition by a structurally distinct CDK7 inhibitor, YKL-1-116, was able to decrease both MYC mRNA level and protein abundance, and this effect was consistently observed in both KURAMOCHI and COV362 cells (Author response image 1).

The CDK12/13 inhibitor THZ531 exhibits quite unique effects on MYC expression. At low doses of THZ531, MYC expression is induced while higher doses of THZ531 cause MYC repression (Author response image 1). The kinetics of inhibition of MYC expression by THZ531 is subtly different among these cell lines: MYC expression was induced by THZ531 at low dosage (0.05 or 0.1 μM) and then suppressed by higher concentrations (0.5 μM and above) in KURAMOCHI cells; while in COV362 cells, MYC expression was induced by THZ531 at 0.5 μM and above but reduced at 5 μM. Therefore, we think that THZ531 impacts MYC expression with a similar pattern in different cell lines, although with subtle differences in the required compound dosages.

Author response image 1
CDK7 inhibitor (YKL-1-116) and CDK12/13 inhibitor (THZ531) have different effects on MYC expression.

Indicated ovarian cancer cell lines were treated with increasing concentrations of YKL- 1-116 or THZ531, with cell lysates prepared following 6 hours of treatment.

Pursuant to this, the authors have not conclusively demonstrated that CDK7, CDK12, or CDK13 are the relevant targets in this study. At minimum, the authors should test point mutants of CDK7 and/or CDK12 and CDK13 to confirm that they are the relevant targets in these cell lines.

We agree that the raised point is extremely critical. To further validate that CDK7 is the relevant target of THZ1 or YKL-1-116, we now ectopically overexpress a mutant form of CDK7 (C312S) that cannot be covalently bound by the inhibitors and thus confer resistance to the compounds (Kwiatkowski et al., 2014). We find that the mutant CDK7 (C312S), but not the wild type, effectively rescues THZ1-induced MYC downregulation (Figure 4—figure supplement 2A). Concurrently, other readouts of CDK7 activity, such as CTD phosphorylation of RNAPII at Ser 5 and Ser 7, are also rescued by this CDK7 mutant. These data indicate that CDK7 is indeed a target of THZ1 in downregulating MYC.

Although overexpression of mutant CDK7 (C312S) fails to rescue cell growth inhibition conferred by THZ1 (see Author response image 2A), it significantly rescues cell growth inhibition by the selective CDK7 inhibitor, YKL-1-116 (Author response image 2B). We would expect that a greater degree of rescue might require the elimination of endogenous CDK7. In addition, considering that THZ1 targets both CDK7 and CDK12/13, we speculate that co-overexpression of mutant CDK7 (C312S), mutant CDK12 (C1039S), and mutant CDK13 may rescue cell growth inhibition by THZ1. Unfortunately, we failed to successfully achieve overexpression of CDK12/13, likely due to their large sizes (1490 aa for CDK12, and 1512 aa for CDK13).

Author response image 2
Overexpressing CDK7 C312S mutant rescued cell growth inhibition by YKL-1-116.

OVCAR8 cells were infected with or without HA-tagged CDK7 WT or C312S, followed by G418 selection and doxycycline induction, and then were treated with increasing concentrations of (A) THZ1, top panel, or (B) YKL- 1-116, bottom panel.

Left. Cells were then subjected to CellTiter-Glow Luminescent Cell Viability Assay after 72 hours of treatment and data were represented as mean ± SD of biological triplicates. Right. Cells were treated with increasing concentrations of THZ1 (0, 50, 100, 200 nM) or of YKL-1-116 (0, 62.5, 125, 250, 500, 1000 nM), and subjected to clonogenic cell growth. Cells were fixed 7 days after treatment and stained with crystal violet.</Author response image 2 title/legend>

2) The relative impact of the drug on MYC and MCL-1 vs. downstream targets is not clear. Can this be delineated further? Genetic rescue of MYC and MCL1 should also be performed to provide more than correlative data.

We have found that (1) MYC or MCL-1 is functionally important for ovarian cancer cell growth, and (2) MYC expression is efficiently downregulated by CDK7 inhibition – an observation that we are now able to rescue through expressing the mutant CDK7 (C312S) that is defective in binding to and suppressing CDK7. It is likely that the downregulation of MYC, MCL-1, and others, act collectively to account for the cell growth phenotypes induced by CDK7 inhibition.

Upon the suggestion, we study if ectopic expression of MYC could rescue cell growth inhibition conferred by CDK7 inhibitors. We overexpressed MYC, or MCL-1, or MYC plus MCL-1 in ovarian cancer cells by lentiviral infection (see Author response image 3).

Author response image 3
Overexpression of GFP/ MYC/ MYC-1/ MYC+MCL-1 in OVCAR8 cells.

Cells were infected with pCDH-GFP, or MYC, or MCL-1, or MYC plus MCL-1 twice, followed by selection with puromycin (1 ug/ml, 2 days). Cells were then seeded at 0.2 million per well in a 12-well plate, then treated with increasing concentrations of THZ1 (0, 31.25, 62.5, 125, 250, 500 nM) the next day, with cell lysates prepared following 6 hours of treatment.</Author response image 3 title/legend>

Next, we examined whether overexpressing MYC, or MCL-1, or MYC plus MCL-1 could rescue cell growth inhibition observed upon treatment with THZ1 or YKL-1-116. This has turned out to be technically difficult, because we found that overexpressing MYC inhibits the growth of ovarian cancer cells (cells that already have high endogenous level of MYC expression; see Author response image 4). The observation indicates that too much MYC protein – such as by overexpressing MYC in cells with MYC-amplification – could be toxic. Notably, similar observations have been documented in a number of models (Bissonnette et al., 1992; Muthalagu et al., 2014; Topham et al., 2015). We therefore have to abandon this line of experimentation.

Author response image 4
Overexpression of MYC inhibits cancer cell growth.

OVCAR8 cells were infected with pCDH-GFP/ MYC/ MYC-1/ MYC+MCL-1 twice, then selected with puromycin 1 ug/ml for 2 days. Cells were seeded at 5,000 per well in a 12-well plate, then treated with DMSO or increasing concentrations of THZ1 or YKL-1-116 the next day. Pictures were taken for cells treated with DMSO control for 6 days prior to cell fixation.

Moreover, the effects observed of the MYC targeting sgRNAs is attributed to the loss of MYC expression, however, a major problem with targeting sgRNAs to amplified regions in the genome is the induction of DNA damage and cell death, independent of the sgRNA target (Aguirre AJ et al., 2017 Cancer Discovery). Are these cells responsive to shRNA mediated depletion of MYC or can the effect be rescued by MYC overexpression?

This is an excellent question. We now analyzed the large-scale CRISPR screen performed by Broad Institute, a study where they developed the CERES computational model to reduce the false-positive differential dependencies caused by multiple DNA breaks resulted from targeting amplified regions (Meyers et al., 2017). In the analysis illustrating MYC dependency in a total 484 cancer cell lines, there is no statistical correlation between MYC dependency values and with MYC copy number (see Figure 1—figure supplement 1A), indicating the successful computational elimination of effects introduced by targeting amplified MYC. Notably, ovarian cancer cells overall demonstrate a high dependence on MYC (indicated by the low CERES values), and as expected, MYC dependency is highly correlated with cell dependency on MAX – a partner of MYC for transcriptional regulation (Figure 1—figure supplement 1B). These data further confirm the functional roles of MYC for ovarian cancer cell proliferation.

3) The PDX data is the most exciting. One wonders if there are agents used in R/R ovarian cancer which can be tested in combination to determine if the first studies should be monotherapy or combination studies.

We performed a combination study with THZ1 and PARP inhibitor Olaparib, a FDA-approved drug in relapsed ovarian cancer irrespective of BRCA1/2 status. We have now included the data in the revised manuscript (see Figure 5A-C). Although the single-agent efficacy of Olaparib is quite modest, PARP inhibition enhances the tumor growth inhibition by THZ1 in 5 out of 11 PDX models (DF-106, 118, 86, 68, and 181).

https://doi.org/10.7554/eLife.39030.023

Article and author information

Author details

  1. Mei Zeng

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5316-3522
  2. Nicholas P Kwiatkowski

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Data curation, Supervision, Project administration, Writing—review and editing
    Competing interests
    is an inventor on a patent application covering THZ1 (patent application number WO/2014/063068 A1), which is licensed to Syros Pharmaceuticals
  3. Tinghu Zhang

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Resources, Supervision, Project administration
    Competing interests
    is an inventor on a patent application covering THZ1 (patent application number WO/2014/063068 A1), which is licensed to Syros Pharmaceuticals
  4. Behnam Nabet

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Data curation, Software, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6738-4200
  5. Mousheng Xu

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Medicine, Harvard Medical School, Boston, United States
    Contribution
    Data curation, Software, Formal analysis, Methodology
    Competing interests
    No competing interests declared
  6. Yanke Liang

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  7. Chunshan Quan

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  8. Jinhua Wang

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared
  9. Mingfeng Hao

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  10. Sangeetha Palakurthi

    Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Data curation, Supervision, Investigation
    Competing interests
    No competing interests declared
  11. Shan Zhou

    Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  12. Qing Zeng

    Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  13. Paul T Kirschmeier

    Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared
  14. Khyati Meghani

    Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared
  15. Alan L Leggett

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  16. Jun Qi

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Medicine, Harvard Medical School, Boston, United States
    Contribution
    Resources, Funding acquisition
    Competing interests
    No competing interests declared
  17. Geoffrey I Shapiro

    Early Drug Development Center, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Resources, Supervision
    Competing interests
    No competing interests declared
  18. Joyce F Liu

    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Resources, Supervision
    Competing interests
    No competing interests declared
  19. Ursula A Matulonis

    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Resources, Formal analysis, Funding acquisition
    Competing interests
    No competing interests declared
  20. Charles Y Lin

    1. Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, United States
    2. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing—review and editing
    For correspondence
    Charles.Y.Lin@bcm.edu
    Competing interests
    is a consultant of Jnana Therapeutics and is a shareholder and inventor of IP licensed to Syros Pharmaceuticals
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9155-090X
  21. Panagiotis A Konstantinopoulos

    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Resources, Supervision, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    Panagiotis_Konstantinopoulos@DFCI.HARVARD.EDU
    Competing interests
    No competing interests declared
  22. Nathanael S Gray

    1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing—review and editing
    For correspondence
    nathanael_gray@dfci.harvard.edu
    Competing interests
    is an inventor on a patent application covering THZ1 (patent application number WO/2014/063068 A1), which is licensed to Syros Pharmaceuticals. Is a scientific founder and equity holder of Syros Pharmaceuticals, C4 Therapeutics, Petra Pharma, Gatekeeper Pharmaceuticals, and Soltego
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5354-7403

Funding

National Cancer Institute (NIH R01 CA197336-02)

  • Nathanael S Gray

National Cancer Institute (NIH R01 CA179483-02)

  • Nathanael S Gray

U.S. Department of Defense (W81XWH-14-OCRP-OCACAOC140632 award)

  • Panagiotis A Konstantinopoulos

Cancer Prevention and Research Institute of Texas (RR150093)

  • Charles Y Lin

National Cancer Institute (R01CA215452-01)

  • Charles Y Lin

American Cancer Society (Postdoctoral Fellowship PF-17-010-01-CDD)

  • Behnam Nabet

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Dr. Yubao Wang at Dana-Farber Cancer Institute for fruitful discussions of the study and his critical reading of the manuscript. The study was supported by NIH R01 CA197336-02 (NSG), R01 CA179483-02 (NSG), Department of Defense W81XWH-14-OCRP-OCACAOC140632 award (PAK), Cancer Prevention Research Institute of Texas RR150093 (CYL), NIH and NCI R01CA215452-01 (CYL), Pew-Stewart Scholar for Cancer Research (CYL), and American Cancer Society Postdoctoral Fellowship PF-17-010-01-CDD (BN).

Ethics

Animal experimentation: All animal experiments were conducted in accordance with the animal use guidelines from the NIH and with protocols (Protocol # 11-044) approved by the Dana-Farber Cancer Institute Animal Care and Use Committee. Full details are described in Materials and Methods - Animal Studies.

Senior Editor

  1. Charles L Sawyers, Memorial Sloan-Kettering Cancer Center, United States

Reviewing Editor

  1. Ross L Levine, Memorial Sloan-Kettering Cancer Center, United States

Publication history

  1. Received: June 8, 2018
  2. Accepted: November 11, 2018
  3. Accepted Manuscript published: November 13, 2018 (version 1)
  4. Version of Record published: November 23, 2018 (version 2)

Copyright

© 2018, Zeng et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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