Mutations in the gene encoding the guanosine triphosphatase KRAS are common drivers of various cancers. Because most mutant KRAS proteins are currently too difficult to therapeutically target, alternative targets in KRAS-mutant tumors must be identified. Given a previous observation that KRAS-mutant pancreatic cancers (PDACs) rely on the transcription factor MYC, Blake et al. screened for inhibitors that decreased MYC protein abundance. They found that an inhibitor of the cell cycle–associated kinase CDK9 decreased MYC abundance at both the mRNA and protein levels in a manner independent of KRAS signaling itself. This finding reveals a potential therapeutic target for patients with KRAS-mutant PDAC and possibly also those with more generally MYC-dependent cancers.

Stabilization of the MYC oncoprotein by KRAS signaling critically promotes the growth of pancreatic ductal adenocarcinoma (PDAC). Thus, understanding how MYC protein stability is regulated may lead to effective therapies. Here, we used a previously developed, flow cytometry–based assay that screened a library of >800 protein kinase inhibitors and identified compounds that promoted either the stability or degradation of MYC in a KRAS-mutant PDAC cell line. We validated compounds that stabilized or destabilized MYC and then focused on one compound, UNC10112785, that induced the substantial loss of MYC protein in both two-dimensional (2D) and 3D cell cultures. We determined that this compound is a potent CDK9 inhibitor with a previously uncharacterized scaffold, caused MYC loss through both transcriptional and posttranslational mechanisms, and suppresses PDAC anchorage-dependent and anchorage-independent growth. We discovered that CDK9 enhanced MYC protein stability through a previously unknown, KRAS-independent mechanism involving direct phosphorylation of MYC at Ser 62 . Our study thus not only identifies a potential therapeutic target for patients with KRAS-mutant PDAC but also presents the application of a screening strategy that can be more broadly adapted to identify regulators of protein stability.

Our previous studies found that KRAS regulation of MYC protein stability in KRAS-mutant PDAC involved both ERK-dependent and ERK-independent mechanisms but not PI3K-Akt signaling ( 14 , 26 ). To further elucidate the mechanisms by which KRAS regulates MYC protein stability, we developed and applied a MYC protein degradation screen in KRAS-mutant PDAC cells ( 14 ). To identify previously unknown protein kinase-dependent mechanisms that regulate MYC protein stability, we then screened the Published Kinase Inhibitor Set (PKIS) of adenosine triphosphate (ATP)–competitive protein kinase inhibitors ( 29 , 30 ). This approach, together with two other screening strategies, identified a MAPK kinase 5 (MEK5)–ERK5 compensatory mechanism induced by inhibition of KRAS-ERK1/2 function ( 14 ). In this study, we focused on the methodology for application of the screen and the experimental strategies to validate kinase inhibitors that either stabilize MYC protein or promote its degradation. Our evaluation of one compound that stimulated loss of MYC protein abundance identified cyclin-dependent kinase 9 (CDK9) as a previously unknown regulator of MYC protein stability.

Mutationally activated KRAS promotes increased MYC expression by stimulating MYC gene transcription and by promoting MYC protein stability ( 14 , 26 ). KRAS effector signaling promotes MYC protein stability through extracellular signal–regulated kinase (ERK) mitogen-activated protein kinase (MAPK) phosphorylation on MYC residue Ser 62 ( 27 ). Phosphorylated Ser 62 then facilitates glycogen synthase kinase 3 (GSK3β)–mediated phosphorylation of MYC at Thr 58 , and subsequent dephosphorylation of Ser 62 by the tumor suppressor protein phosphatase 2 (PP2A) promotes E3 ligase FBXW7-dependent MYC degradation. KRAS signaling through the phosphatidylinositol 3-kinase (PI3K) effector pathway, leading to activation of the kinase Akt and concomitant inactivation of GSK3β, represents a second effector signaling mechanism by which KRAS can regulate MYC protein stability. Pharmacologic inhibition of SET, a negative regulator of PP2A, increased MYC degradation and impaired PDAC tumorigenic growth, supporting the therapeutic value of targeting MYC protein degradation ( 28 ).

The MYC transcription factor drives a multitude of proliferative and progrowth phenotypes ( 18 ). Strategies to inhibit MYC function have included inhibition of MYC transcription (for example, using bromodomain inhibitors like JQ1) ( 19 , 20 ), inhibition of MYC/MAX dimerization ( 21 , 22 ), and targeting of expression of MYC-regulated genes ( 23 ) or MYC-associated metabolic vulnerabilities ( 24 ). Of these strategies, only bromodomain inhibitors have entered clinical trials, but their relative lack of selectivity for MYC transcription remains a concern ( 25 ).

The interplay and interdependency of the RAS and the MYC oncogenes in driving cancer development and maintenance is well established. This association was first demonstrated when it was shown that MYC overexpression was necessary to support RAS transformation of rodent fibroblasts ( 8 ). MYC expression is frequently increased in many cancers, most commonly by gene amplification or increased gene transcription ( 9 ). Subsequent studies in genetically engineered mouse models of cancer demonstrated the essential role of MYC in KRAS-driven oncogenesis ( 10 , 11 ), and genetic suppression of MYC impairs KRAS-driven PDAC ( 12 – 15 ). Moreover, overexpression of MYC alone was sufficient to phenocopy mutant KRAS and drive development of metastatic PDAC ( 16 ). Thus, targeting MYC could be an effective therapeutic strategy for MYC-dependent cancers such as KRAS-mutant PDAC. However, similar to RAS, MYC has also been considered undruggable because of a surface topology that lacks deep pockets for design of potent small-molecule binders ( 17 ).

The main genetic driver of PDAC initiation, progression and maintenance is mutational activation of the KRAS oncogene, which is found in ~95% of PDAC ( 3 ). Although KRAS was the first cancer gene identified in human cancers over 35 years ago ( 5 ), the effort to effectively target RAS-driven cancers is still ongoing ( 6 , 7 ).

In 2017, pancreatic cancer surpassed breast cancer to become the third leading cause of cancer deaths in the United States ( 1 ). By around 2020, pancreatic cancer is projected to surpass colorectal cancer and become the second leading cause of U.S. cancer deaths ( 2 ). Currently, the 5-year overall survival rate is at an abysmal 8% ( 1 ). Despite a well-defined genetic profile of pancreatic ductal adenocarcinoma (PDAC) ( 3 ), clinically effective targeted therapies remain to be developed, with current treatments limited to conventional cytotoxic drugs ( 4 ).

RESULTS

MYC degradation screen identifies kinase inhibitors that affect MYC protein stability We have previously described our generation and validation of a MYC degradation reporter for use in a cell-based screen to identify protein kinases that regulate MYC protein stability (14). We used the pGPS-LP lentiviral reporter plasmid in which a cytomegalovirus (CMV) promoter regulates expression of a single bicistronic mRNA transcript that encodes both DsRed and enhanced green fluorescent protein (EGFP)–tagged proteins, separated by an internal ribosome entry site (IRES) (31). To construct a reporter capable of monitoring MYC protein abundance, we introduced the complementary DNA (cDNA) sequence encoding human MYC into pGPS-LP to encode an EGFP-MYC fusion protein [designated GPS-MYC; Fig. 1A, (14)]. We then stably infected the KRAS-mutant PDAC cell line MIA PaCa-2 with the GPS-MYC vector and established populations of cells stably expressing DsRed-IRES-EGFP-MYC (hereafter referred to as GPS-MYC cells). EGFP has a long half-life but partially adopts the degradation characteristics of MYC when expressed as a fusion protein (discussed further below). Furthermore, because DsRed is expressed from the same transcript as EGFP-MYC, we can normalize for fluctuations in transcription and proteasome activity on a per cell basis. Thus, we can infer the relative stability of MYC on a per cell basis by examining the EGFP/DsRed ratio (32). Fig. 1 Validation of a MYC degradation reporter. (A) Overview of the GPS-MYC vector. LTR, long terminal repeat. (B) Confocal images of GPS-MYC cells to determine EGFP-MYC subcellular localization to the nucleus, which was visualized by DAPI staining. Scale bars, 20 μm. (C) GPS-MYC cells were treated with CHX for the indicated times, and EGFP-MYC and MYC abundance was measured by immunoblotting (left). The half-lives of EGFP-MYC and endogenous MYC were calculated by fitting the data to a one-phase decay curve. *P < 0.05. (D) GPS-MYC cells were treated with MG132 and CHX for 6 hours, and EGFP and DsRed abundance was measured by flow cytometry. All data are representative of at least three independent experiments. In this study, we first determined whether the exogenously expressed EGFP-MYC displayed properties of endogenous MYC. Like endogenous MYC, EGFP-MYC localization was restricted to the nucleus (Fig. 1B). We showed previously that acute suppression of KRAS caused similar loss of endogenous MYC and EGFP-MYC (14). Although the half-life of EGFP-MYC is slightly longer than that of endogenous MYC (Fig. 1C), demonstrating that EGFP-MYC protein stability is not regulated exactly like endogenous MYC, we reasoned that compounds potent enough to cause loss of the more stable EGFP-MYC would also be able to cause loss of the less stable endogenous MYC. In the GPS-MYC cells, DsRed serves as an internal control for differential expression levels of the cassette among GPS-MYC cells. Treatment of GPS-MYC cells with either cycloheximide (CHX) to inhibit protein synthesis or MG132 to inhibit proteasome-dependent protein degradation altered the EGFP but not the DsRed signal (Fig. 1D). Thus, the ratio of EGFP/DsRed signals provides an accurate determination of MYC protein stability in GPS-MYC cells. After validating GPS-MYC cells as a cell model for monitoring MYC protein stability, we optimized the assay in a 384-well format using liquid handling automation (Fig. 2A). We first evaluated several sources of commercially available 384-well plates for retention of GPS-MYC cells during the automated washing steps in the screen. Although all the plates were cell culture treated, not all of them retained cells well during the washing process. Next, because MIA PaCa-2 cells have a propensity to aggregate, we optimized the type and volume of cell dissociation reagents, as well as shake speed and frequency during the screen to ensure that cells remained in a single-cell suspension throughout the assay. Because MYC has a short half-life in MIA PaCa-2 cells (t 1/2 , ~50 min) (14), a potential confounding assay variable could be differences in the EGFP-MYC signal over the course of assaying an entire plate. To minimize this potential variable, we optimized the sip time (analysis time per well). At the conclusion of our optimization efforts, the assay duration for each 384-well plate was ~45 min, during which the EGFP and DsRed signals remained constant (Fig. 2B). Given that the expression of EGFP-MYC was only ~5-fold above that of endogenous MYC, the resulting EGFP signal was relatively weak, and, coupled with a high background signal, the dynamic range between the vehicle [dimethyl sulfoxide (DMSO)] and CHX controls was also relatively low (~2-fold). Fixation of GPS-MYC cells reduced the EGFP signal even further, rendering the dynamic range too small, so we used live cells in the assay. Despite the relatively narrow dynamic range of the assay between the CHX and DMSO controls, the Z-factors were consistently >0.7, indicating that the assay was robust. All of our screening was then performed on the optimized conditions as described in Materials and Methods. Fig. 2 Optimization of a MYC degradation screen. (A) Schematic of the GPS-MYC screen. (B) GPS-MYC cells were treated with vehicle alone (DMSO) or with MG132 or CHX for 6 hours and analyzed on an IntelliCyt iQue Screener. The data presented are from the first two and last two wells of each control, representing the beginning (0 min) and the end (45 min) of the assay. Data are from one plate and are representative of at least three independent experiments. (C) The GPS-MYC screen was run in duplicate. Data were normalized to control DMSO (blue circles, 0% stabilization) and MG132 (green circles, 100% stabilization), and hits were determined by a cutoff of a 30% average stabilization of the two replicates. The circles representing the hits are shown in purple. The circle size is proportional to the number of events per well. Stabilizing compounds that were evaluated in the paper are labeled in the graph. (D) As described in (C), except the data were normalized to control DMSO (blue circles, 0% destabilization) and CHX (red circles, 100% destabilization). Destabilizing compounds evaluated in the paper are indicated. Given the important nature of protein kinases in regulating MYC protein stability, we screened the GPS-MYC cells with the PKIS of ATP-competitive protein kinase inhibitors (29, 30, 33) to identify previously unknown kinases that regulate MYC protein stability in PDAC. PKIS was generated to maximize structural diversity and the spectrum of kinases inhibited, and the activity profiles of the compounds was published (29, 30, 33), enabling target hypothesis generation as soon as hits are discovered. In total, we screen GPS-MYC cells with this ~800 compound set, treating cells for 6 hours at a concentration of 20 μM, using the IntelliCyt iQue Screener. The screen was run in duplicate to ensure reproducibility. To process the primary screening data, we first separated the data into two parts, percent stabilization and percent destabilization. We calculated percent stabilization by normalizing the results to the DMSO (0%) and MG132 (100%) treatment controls, and we similarly calculated percent destabilization by normalizing the results to the DMSO (0%) and CHX (100%) treatment controls. This enabled determination of the percent effect of the test compounds compared to maximum stabilization or destabilization of EGFP-MYC with MG132 and CHX, respectively. With the cutoff for a hit set at a 30% stabilization or destabilization, we identified 30 compounds that stabilized EGFP-MYC (Fig. 2C and data file S1) and 26 compounds that destabilized EGFP-MYC (Fig. 2D and data file S1). Supporting the validation of the GPS-MYC screen to identify regulators of MYC protein stability, we found that UNC10112687 (SB-590885-AAD; fig. S1A), which inhibits BRAF and CRAF (30), was identified as a compound that destabilized MYC, whereas the GSK3α/β inhibitor UNC10112671 (SB-739452; fig. S1B) stabilized MYC (data file S1). These results are expected because RAF activation of ERK phosphorylates MYC at Ser62 and blocks degradation, whereas GSKβ phosphorylates MYC at Thr58 and promotes degradation (27).

UNC10112731 increases the abundance of endogenous MYC protein We prioritized the top stabilizing compounds based on published specificity profiles (29, 30, 33), diversity of the kinases inhibited, and ease of synthesis and selected and resynthesized UNC10112731 (GW694590A; fig. S1C) (30) for further validation. Consistent with its ability to enhance MYC protein stability, in the initial screen, UNC10112731 increased EGFP-MYC abundance without affecting that of DsRed (Fig. 3A). In a secondary validation screen, treatment of GPS-MYC cells with UNC10112731 showed a dose-dependent increase in EGFP-MYC and endogenous MYC abundance when measured by flow cytometry (Fig. 3B) and immunoblot analyses (Fig. 3C). Next, we sought to determine whether UNC10112731 treatment also stabilized endogenous MYC protein in other PDAC lines and found that endogenous MYC protein abundance was increased to varying degrees (2- to 20-fold) in three of eight cell lines analyzed (Fig. 3D). As expected from its identification using the GPS-MYC reporter, this effect was not due to increased transcription (Fig. 3E). The published kinase targets of UNC10112731 include the receptor tyrosine kinases KIT and platelet-derived growth factor receptor α (PDGFRα) (Fig. 3F and fig. S1C) (29) . However, treatment of PDAC cells with the multityrosine kinase inhibitors imatinib and amuvatinib, both of which have activities against KIT and PDGFRα (34), did not increase endogenous MYC abundance in a dose-dependent manner (Fig. 3G). Therefore, we speculated that the mechanism whereby UNC10112731 stabilizes MYC protein may involve inhibition of additional kinases not identified in the kinase profiling reported for the PKIS compounds. Fig. 3 MYC degradation screen identifies a compound that stabilizes MYC protein. (A) GPS-MYC cells were treated with 20 μM UNC10112731 for 6 hours, and EGFP and DsRed intensities were measured by flow cytometry. Data are from the GPS-MYC screen. (B and C) GPS-MYC cells were treated with increasing concentrations of UNC10112731 for 6 hours and EGFP and DsRed intensities were measured by flow cytometry (B) or immunoblotting (C). (D) KRAS-mutant PDAC cell lines were treated for 6 hours with UNC10112731, and MYC protein abundance was measured by immunoblotting (top), with quantitation by densitometry relative to vehicle control (bottom). *P < 0.05, **P < 0.01, ***P < 0.001 by t test. (E) MIA PaCa-2 cells were treated for 6 hours with UNC10112731, and MYC mRNA expression was measured by quantitative PCR (qPCR). MYC mRNA expression was normalized to that of GAPDH mRNA. a.u., arbitrary units. (F) Kinase selectivity of UNC10112731 as described previously (30). (G) MIA PaCa-2 cells were treated for 6 hours with the indicated compounds, and MYC protein abundance was measured by immunoblot. All data are representative of at least three independent experiments. Data in (D) are presented as means ± SD.

UNC10112785 induces loss of endogenous MYC protein abundance Six of the destabilizer hits from the primary screen shared the same 7-azaindole pharmacophore (fig. S1, D to H), so we synthesized one of those compounds, UNC10112785 (Fig. 4A), for further analysis. We found that treatment of GPS-MYC cells with UNC10112785 did not affect DsRed abundance levels (Fig. 4B) but did decrease the abundance of EGFP-MYC and endogenous MYC (Fig. 4C). Analyses in additional KRAS-mutant PDAC cell lines showed loss of endogenous MYC in all eight cell lines evaluated (Fig. 4D). Examination of the published specificities of five of the 7-azaindole compounds revealed that the shared kinase targets included KIT and dual-specificity tyrosine phosphorylation-regulated kinase 1A/1B (DYRK1A/1B) (Fig. 4E and fig. S1, D to G). However, we found that commercially available inhibitors of either KIT (imatinib and amuvatinib) or DYRK1A/1B (AZ191 and TC-S 7004) failed to induce a dose-dependent loss of endogenous MYC protein (Figs. 3G and 4F), suggesting that, similar to UNC10112731, UNC10112785 may also have additional kinase targets that regulate MYC protein abundance. Fig. 4 UNC10112785 drives MYC protein loss. (A) Chemical structure of UNC10112785. The red circle indicates the position at which different analogs were synthesized. (B) GPS-MYC cells were treated with 20 μM UNC10112785 for 6 hours, and EGFP and DsRed intensity was measured by flow cytometry. Data from the GPS-MYC screen. (C) GPS-MYC cells were treated with UNC10112785 for 6 hours, and EGFP-MYC and MYC abundance was measured by immunoblotting. (D) PDAC cells were treated with UNC10112785, and MYC protein abundance was measured by immunoblot. (E) Kinase selectivity of UNC10112785 as described previously (30). (F) MIA PaCa-2 cells were treated for 6 hours with the indicated compounds, and MYC protein abundance was measured by immunoblot. All data are representative of at least three independent experiments. In 2017, a quantitative, mass spectrometry (MS)–based chemical proteomic assay was used to profile the specificity and potency of 243 clinical kinase inhibitors (35). The sensitive technology of this assay, which evaluates full-length kinases expressed in a cellular environment, enables detection of more kinases (~300) than the kinome coverage of typical in vitro kinase profiling panels. Moreover, it has been shown to be more relevant to inhibitor potency and selectivity assessment than traditional kinase profiling using recombinant proteins. The profiling of PKIS compounds was performed in vitro at only a few concentrations against an incomplete panel of recombinant protein kinases: 196 for PKIS1 and 232 for PKIS2 (29, 30). We reasoned that the kinase targets of UNC10112785 involved in regulation of MYC stability may not have been included in the original profiling of PKIS compounds. Therefore, to further evaluate the kinase selectivity profile of UNC10112785, we applied the multiplexed kinase inhibitor bead (MIB-MS) chemical proteomic assay (36) in MIA PaCa-2 cells (fig. S2A). This assay is based on kinase inhibitor competition assays to assess kinome-wide inhibitor specificity, similar to that described previously (35, 37). MIB-MS in MIA PaCa-2 cells revealed that UNC10112785 appeared to inhibit CDK8, the closely related paralog CDK19 (80% identity), and, to a lesser degree, CDK9 (Fig. 5A). These kinases were not included in the panel evaluated previously with PKIS compounds (29, 30). To validate these activities, we chose CDK8 and CDK19, as well as additional kinases identified by PKIS (Fig. 4E) or MIB-MS (Fig. 5A) for biochemical analyses using recombinant kinases (Table 1). We found that the kinases most potently inhibited in vitro by UNC10112785 were CDK8, CDK19, and CDK9, with median inhibitory concentrations (IC 50 ) of 1.05, 2.67, and 19.9 nM, respectively, and ~10-fold selectivity over other kinases analyzed. Fig. 5 Inhibition of CDK9 by UNC10112785 drives MYC loss. (A) MIA PaCa-2 cells were treated for 1 hour with different concentrations UNC10112785, after which they were lysed and applied to the MIBs column. Kinases were eluted from the column and identified and quantified by LC/MS as described in Materials and Methods. Bars to the left of center line indicate kinases reduced after compound addition. Data are from one experiment, which served to identify hits to validate with reproducibility and explore further. (B) MIA PaCa-2 cells were treated with UNC10112785, and CDK8/19 inhibition was measured by the abundance of phosphorylated STAT1 (pSTAT1). (C and D) MIA PaCa-2 cells were treated with the indicated compounds for 6 hours, and CDK8/19 or CDK9 inhibition was measured by pSTAT1 (C) and pPol II (D). (E) MIA PaCa-2 cells were treated for 72 hours with the indicated compounds, and cell proliferation was measured by cell count, normalized to that cells treated with the vehicle control (DMSO). 2D, two-dimensional. (F) MIA PaCa-2 cells suspended in 3% agarose were treated for 72 hours with the indicated compounds, and cell proliferation was measured by alamarBlue, normalized to cells treated with vehicle (DMSO). All data are representative of at least three independent experiments unless otherwise indicated. Data in (E) and (F) are presented as means ± SD. Table 1 Biochemical profiling of UNC10112785. The activity of UNC10112785 was tested against a panel of kinases that had previously been identified in either the published PKIS data or the MIB-MS assay using SelectScreen Kinase Profiling (Thermo Fisher Scientific). View this table: Consistent with our MIB-MS and biochemical analyses, we found that treatment of MIA PaCa-2 cells with UNC10112785 reduced the phosphorylation of signal transducer and activator of transcription 1 (STAT1) at Ser727 (pSTAT1), a marker of CDK8/19 activity (Fig. 5B) (38). However, pSTAT1 was maximally reduced by 100 nM, whereas MYC loss was not observed until 1 μM, suggesting that CDK8/19 inhibition may not be the basis for MYC loss. To address this possibility, we treated MIA PaCa-2 cells with Senexin B, a structurally distinct potent and selective CDK8/19 inhibitor (39), which resulted in a reduction in pSTAT1 but not MYC protein levels (Fig. 5C). Senexin B is not a CDK9 inhibitor, as indicated by the absence of a reduction in myeloid leukemia–1 (MCL-1), a well-validated transcriptional target of CDK9 (40). Thus, we conclude that, although UNC10112785 is a potent inhibitor of CDK8/19 in vitro and in cells, MYC loss was not caused by inhibition of CDK8/19.

Inhibition of CDK9 drives MYC loss caused by UNC10112785 Given that UNC10112785 inhibited CDK9 in the MIB-MS analyses, and this was verified by biochemical analyses, we next addressed the possibility that inhibition of CDK9 was responsible for UNC10112785-driven loss of MYC. First, we evaluated the activity of a structurally distinct CDK9-selective inhibitor, NVP2 (41). NVP2 treatment reduced the phosphorylation of RNA polymerase II (pPol II) at Ser2, another marker of CDK9 activity (40), as well as protein abundance of MCL-1 and MYC. However, NVP2 did not reduce that of pSTAT1, indicating that it inhibited CDK9 but not CDK8/19 (Fig. 5C). We then synthesized 10 structurally related analogs of UNC10112785 to determine the relative contributions of their CDK8/19 and CDK9 inhibitory activities to driving MYC loss (table S1). Some analogs lost the ability to decrease MYC, whereas others were more potent at this than the parent compound (Fig. 5D and fig. S2B). Retention of the ability to suppress pPol II and MCL-1 (CDK9 inhibition) correlated with the loss of MYC, whereas some analogs retained the ability to inhibit pSTAT1 (CDK8/19 inhibition) yet lost the ability to reduce MYC. These data support the possibility that UNC10112785 inhibition of CDK9 rather than CDK8/19 is the basis for its reduction of MYC protein abundance. To further explore the relative contribution of CDK8/19 versus CDK9 inhibition to loss of MYC, we chose two analogs for additional characterization. Whereas both analogs retained low nanomolar activities against CDK8/19 (fig. S2C), the potency of UNC5668 to inhibit CDK9 in vitro was increased by 7.5-fold compared with parent compound UNC10112785, whereas that of UNC5577 was decreased by 5.3-fold. MIB-MS inhibitor competition analyses verified their altered CDK9 activities in MIA PaCa-2 cells (fig. S2D). Consistent with their relative CDK9 potencies in vitro, we found that UNC5668 exhibited 10-fold increased potency over UNC10112785 in cells. Treatment of MIA PaCa-2 cells with 100 nM of UNC5668 suppressed both CDK9 signaling (pPol II and MCL-1) and MYC protein abundance (Fig. 5D). In contrast, treatment with 100 nM UNC5577 suppressed CDK8/19 signaling (pSTAT1) but not pPol II, MCL-1, or MYC (Fig. 5D). Last, because we showed previously that MYC is essential for the growth of PDAC cell lines (14), we next evaluated the ability of these analogs to suppress cell proliferation. Consistent with their ability to reduce MYC abundance, all compounds with CDK9 but not CDK8/19 inhibitory activity reduced proliferation of MIA PaCa-2 cells on plastic (Fig. 5E) and colony formation in soft agar (Fig. 5F) at concentrations where potent inhibition of CDK9 signaling was observed. Together, these data indicate that inhibition of CDK9 rather than CDK8/19 is responsible for UNC10112785-driven MYC loss.