Combination Therapies with CDK4/6 Inhibitors to Treat KRAS-mutant Pancreatic Cancer

Abstract

Mutational loss of CDKN2A (encoding p16INK4A) tumor suppressor function is a key genetic step that complements activation of KRAS in promoting the development and malignant growth of pancreatic ductal adenocarcinoma (PDAC). However, pharmacologic restoration of p16INK4A function with inhibitors of CDK4 and CDK6 (CDK4/6) has shown limited clinical efficacy in PDAC. Here, we found that concurrent treatment with both a CDK4/6 inhibitor (CDK4/6i) and an ERK MAPK inhibitor (ERKi) synergistically suppresses the growth of PDAC cell lines and organoids by cooperatively blocking CDK4/6i-induced compensatory upregulation of ERK, PI3K, anti-apoptotic signaling, and MYC expression. Based on these findings, a Phase I clinical trial was initiated to evaluate the ERKi ulixertinib in combination with the CDK4/6i palbociclib in patients with advanced PDAC (NCT03454035). As inhibition of other proteins might also counter CDK4/6i-mediated signaling changes to increase cellular CDK4/6i sensitivity, a CRISPR-Cas9 loss-of-function screen was conducted that revealed a spectrum of functionally diverse genes whose loss enhanced CDK4/6i growth inhibitory activity. These genes were enriched around diverse signaling nodes, including cell cycle regulatory proteins centered on CDK2 activation, PI3K-AKT-mTOR signaling, SRC family kinases, HDAC proteins, autophagy-activating pathways, chromosome regulation and maintenance, and DNA damage and repair pathways. Novel therapeutic combinations were validated using siRNA and small molecule inhibitor-based approaches. Additionally, genes whose loss imparts a survival advantage were identified (e.g., RB1, PTEN, FBXW7), suggesting possible resistance mechanisms to CDK4/6 inhibition. In summary, this study has identified novel combinations with CDK4/6i that may have clinical benefit to PDAC patients.

Introduction

Pancreatic cancer is the third-highest cause of cancer-related mortality in the US (1). The majority (>85%) of pancreatic malignancies are of the pancreatic ductal adenocarcinoma (PDAC) type. The current standards-of-care for PDAC are minimally effective and comprised of conventional cytotoxic drugs (2). Despite a now well-delineated genetic profile, to date, no clinically effective targeted therapies for PDAC have been developed.

The genetic basis of PDAC is well-established (3). Mutational activation of the KRAS oncogene (~95%) is the initiating step and is essential for maintenance of PDAC growth. The recent approval of the first direct KRAS inhibitor for lung cancer (sotorasib), selective for one KRAS mutant (G12C) (4), supports the potential therapeutic impact of anti-KRAS therapies for PDAC. However, as KRAS^G12C mutations comprise only 2% of KRAS mutants in PDAC, distinct strategies are required for the majority of patients.

A second key genetic event in PDAC development (occurring in greater than 90% of pancreatic cancers) involves genomic deletion or promoter hypermethylation and silencing of the CDKN2A tumor suppressor gene (5). In mouse model studies, Kras mutation alone initiated premalignant pancreatic ductal lesions at complete penetrance, with delayed low frequency formation of metastatic pancreatic cancer (<10%) (6) However, Kras mutation coupled with Ink4a loss induced rapid onset of metastatic PDAC (7).

The primary consequence of CDKN2A loss in PDAC is loss of expression of the cell cycle inhibitory protein p16^INK4A, which functions through inhibition of the highly related cyclin-dependent kinases CDK4 and CDK6 (8). Binding of the D-type cyclins (e.g., cyclin D1) to CDK4/6 relieves this inhibition, allowing CDK4/6 to phosphorylate and inactivate the retinoblastoma (RB) tumor suppressor. Once hyperphosphorylated, RB can no longer sequester and inactivate the E2F family of transcription factors, permitting E2F-driven transcriptional activation of genes that drive the G1/S transition and cell growth.

KRAS also regulates CDK4/6 activity through the canonical RAF-MEK-ERK MAPK effector signaling network to stimulate transcriptional activation of CCND1 (encodes cyclin D1) (9). KRAS additionally blocks cyclin D1 degradation through AKT-mediated inactivation of GSK3β via the PI3K-AKT effector signaling network. Therefore, KRAS effector signaling and CDKN2A inactivation mechanistically converge to inactivate RB.

The critical role of CDKN2A loss of function during PDAC tumorigenesis supports the therapeutic value of pharmacologic agents that phenocopy the effect of p16^INK4A to inhibit the growth of KRAS-driven PDAC tumors. Currently, three CDK4/6 inhibitors (palbociclib, abemaciclib, ribociclib) have been approved for the treatment of estrogen receptor (ER)-positive, HER2 receptor tyrosine kinase (RTK)-negative breast cancer in combination with ER-targeted therapies (8). A fourth CDK4/6 inhibitor (trilaciclib) has been approved as a myeloprotective agent combined with chemotherapy in small-cell lung cancer, while another (lerociclib) is currently under clinical evaluation for multiple indications. Preclinical evaluation of CDK4/6 inhibitors in KRAS-mutant PDAC cell lines (10-12) and patient-derived xenograft (PDX) tumors (13) has shown single agent anti-tumor activity. Growth inhibitory activity was significantly improved when combined with inhibitors of specific components of KRAS effector signaling (10,14,15).

Despite promising findings from preclinical studies, the outcomes of clinical evaluation of CDK4/6 inhibitors in PDAC has been disappointing. In a phase II trial (NCT02981342), abemaciclib treatment alone or in combination with the PI3K inhibitor LY3023414 did not show significant improvement in overall survival. A phase I evaluation of ribociclib in combination with the mTOR inhibitor everolimus (NCT02985125) also resulted in no significant clinical response.

In this study, we further assessed CDK4/6 as an effective therapeutic target in PDAC, determined that ERK inhibition effectively enhances CDK4/6 inhibitor cytotoxicity, and conducted a CRISPR-Cas9 loss-of-function screen to identify novel synergistic combinations that further sensitize pancreatic cancer to CDK4/6 inhibition that may improve clinical outcomes.

Materials and Methods

Cell lines and culture conditions

Pa01C, Pa02C, Pa03C, Pa04C, Pa14C and Pa16C cell lines were provided by A. Maitra (MD Anderson Cancer Center). The remaining PDAC cell lines were obtained from American Type Culture Collection (ATCC, Manassas, VA, USA) and were maintained in recommended media (DMEM or RPMI 1640 supplemented with 10% FBS and penicillin/streptomycin). Previously established organoids derived from pancreatic cancer were provided by David Tuveson (Cold Spring Harbor Laboratory). No patient samples were used in this study. All cell lines were maintained in a humidified chamber with 5% CO2 at 37°C. Pancreatic organoid cells were seeded in growth factor–reduced Matrigel (Corning) domes and fed with complete human feeding medium: advanced DMEM/F12-based WRN condition medium (L-WRN, ATCC CRL-3276), 1× B27 supplement, 10 mM HEPES, 0.01 μM GlutaMAX, 10 mM nicotinamide, 1.25 mM N-acetylcysteine, 50 ng ml–1 hEGF, 100 ng ml–1 hFGF10, 0.01 μM hGastrin I, 500 nM A83-01, 1 μM PGE2 and 10.5 μM Y27632. Colorectal cancer organoids were maintained as described (16). All cell lines were validated using short-tandem-repeat (STR) profiling and confirmed regularly as mycoplasma-negative.

Antibodies, plasmids and chemical reagents

Chemical reagents used were Calcein AM, Caspase-Glo 3/7, and AlamarBlue (Invitrogen), ATP-Lite (Perkin Elmer), MTT (Sigma). Antibodies for total RB, phospo-RB (780) (C84F6), phospho-RB (S807/S811) (D20B12), total RB (4H1), total ERK (L34F12), phospho-ERK (T202/204) (D13.14.4E XP), phospho-p90RSK (T359/S363) (9344), CDK4 (D9G3E), and CDK6 (D4S8S) from Cell Signaling Technologies (Danvers, MA, USA); BAP1 from Santa Cruz Biotechnology (Dallas, TX, USA); β-tubulin from Sigma. Each was used at a 1:1000 dilution, excepting Santa Cruz antibodies at a 1:100 dilution. Blots were probed with species-appropriate HRP-conjugated secondary antibodies (Sigma) at a 1:2000 dilution and measured on the Biorad Chemidoc MP. Quantification performed using the Biorad Image Lab software. The pLentiPGK Blast DEST ERKKTRmRuby2 plasmid was a gift from Markus Covert (Addgene plasmid # 90231; http://n2t.net/addgene:90231 ; RRID:Addgene_90231). The pBABE-puro mCherry-EGFP-LC3B retroviral expression vector was a gift from J. Debnath (Addgene plasmid # 22418; http://n2t.net/addgene:22418; RRID:Addgene_22418).(17) Palbociclib, ribociclib (LEE001), quisinosat, saracatenib, hydroxychloroquine, ABT-263, erlotinib, bosutinib, digitoxin, KU60019 were obtained from SelleckChem (Houston, Tx, USA). Lerociclib was synthesized by G1 Therapeutics. Abemaciclib (LY2835219), PF-06873600, R428, and BMS303141 were obtained from MedChemExpress. Methotrexate was purchased from Sigma. AMG510 was provided by Deciphera Pharmaceuticals. SCH772984 was provided by Merck. Ulixertinib/BVD523 was provided by Biomed Valley Discoveries.

Viability studies and synergy calculations

Cells in anchorage-dependent assays were plated in flat 96-well plates at a density of 1500 cells/well (or 2000 cells/well for Pa16C). For anchorage-independent assays, cells were mixed with SeaPrep Agarose (final concentration 1% agar in complete media) and plated on a basement of solid bacto-agar (0.6%) at a final count of 5000 cells/well. A liquid layer of media was added on top of the solidified agar layers to accept dispensed drugs. Compounds were added using a Tecan D300 automated dispenser (HP, USA) 24 hours after plating. Compounds were generally plated in three-fold dilution schemes at concentrations to encapsulate the entire dose-response curve. For measuring cell viability using direct cell counting, plates were washed once with 1x PBS, then incubated with a 1:5000 dilution of live cell dye Calcein AM in PBS for 30 minutes at 30°C. Cells were then counted using the SpectraMax MiniMax (Molecular Devices, Sunnyvale, CA, USA). The Spectramax software automated counting of live cells. Viability assays using other methods were used as a comparison to direct cell counting, where indicated. For MTT assays, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (Sigma) was added at 0.4 mg/ml in PBS to cell media, incubated at 37°C for 2 hours, then the wells rinsed with PBS. To read, 100 uL DMSO was added per well, and absorbance read at 590 nm. For Alamar Blue (Invitrogen), reagent was added at a 1:10 dilution and incubated for 3 hours at 37°C, then read at 535 nm excitation and 590 nm emission fluorescence. A third metabolic-based viability assay, using ATP Lite (Perkin-Elmer), detects ATP levels as a proxy for cell viability, and was used according to the manufacturers recommendations. Caspase activation in organoids was measured using Caspase 3/7 Glo (Promega) using the manufacturers recommended concentrations and carefully mixing with the Matrigel/organoid layer by manual pipetting. Graphpad Prism was used to calculate EC₅₀ values and display dose-response curves. BLISS scores were calculated using the BLISS equation (18). Three-dimensional heatmaps were generated using the R package plot.ly.

Colony formation and organoid assays

For anchorage-dependent colony formation assays, cells were plated in 6-well plates at a count of 500 cells/well. Compound was added after 24 hours, and cells were allowed to grow for 14 days. To visualize colonies, wells were washed with PBS, stained and fixed with 4% formaldehyde and crystal violet for 15 min, washed extensively in water, then read on a Typhoon FLA 9500 (GE, USA). Colonies were quantified using ImageJ by calculating percent area covered. Organoids were plated and drugged as previously described (19), grown for 10 days, and imaged on the SpectraMax i3x MiniMax 300 imaging cytometer. Cell viability was measured using Cell Titer Glo 3D Cell Viability assay (Promega) according to the manufacturer’s instructions on the SpectraMax i3x plate reader.

siRNA transfections

Before siRNA transfection, 2 x 10⁵ PANC-1 cells, 2.5 x 10⁵ Pa14C cells, or 3 x 10⁵ MIA PaCa-2 cells were seeded into individual wells of a 6-well plate. The next day, culture medium was replaced. To suppress CDK4 or CDK6, 30 pmol of siRNA were added to 200 μl of Opti-MEM medium followed by 2.5 μl of RNAiMax. Two siRNAs targeting CDK4 (siCDK4 1, Dharmacon J-003238-13, and siCDK4 2, Dharmacon J-003238-15) and CDK6 (siCDK6 1, Dharmacon J-003240-10, and siCDK6 2, Dharmacon J-003238-12) and a nontargeting siRNA (siNT D-001810-01-20) were used in all experiments. To concurrently suppress CDK4 and CDK6, 30 pmol of siCDK4 1 and 30 pmol of siCDK6 1 were combined. The mixtures were incubated for 8-10 minutes before being added dropwise to cells. The next day, cells were collected and seeded into proliferation assays at 3 x 10⁵ cells per well of a 96-well plate, or 2 x 10⁵ cells were plated into a 6-well plate for immunoblot analyses. For combination experiments, cells were treated with ERKi the next day. Proliferation assays and immunoblot analyses were performed as described after an additional 72 hours.

Reverse phase protein array

RPPA experiments were conducted as previously described (19). Briefly, cells were grown in 6-well plates and subdivided so to not exceed 80% confluency. Drugs were added 24 hours after initial plating and replenished after each subdivision. At the indicated timepoints, cells were harvested, lysate collected, and analyzed as previously described (19). All unique conditions were collected in quadruplicate. The median of all replicates was calculated, then samples were normalized as the log2 fold-change between drug or control treatment.

Western blotting and senescence assay

Cell lysates were collected in RIPA buffer (50 mM TRIS-HCL pH 7.4, 50 mM NaCl, 2 mM EDTA, 0.1% SDS supplemented with protease inhibitor (Complete EDTA-Free, Roche) and phosphatase inhibitor (Phosphatase Inhibitor Cocktail Sets 1 and 2, Millipore). Equal amount of lysate (18 μg) was boiled in SDS protein loading buffer supplemented with 10 mM DTT, separated by SDS-PAGE, transferred to Immobilon-FL PVDF membrane (Millipore, Billerica, MA, USA), and blotted for indicated protein.

Senescence was detected using the β-galactosidase based Senescence Detection Kit (ab65351, Abcam), using the recommended protocol. Cells were imaged and photographed on an Evos M7000. Separately, the CellEvent Senescence Green Detection Kit (ThermoFisher) was used to stain senescent cells. Cells were plated in 6-well plates, treated with compound for 7 days and passaged as necessary to maintain under 80% confluency, then replated into 96-well plates and stained using the manufacturer’s protocol.

pERK TR-FRET assay

ERK phosphorylation was detected using the Advanced phospho-ERK (Thr202/Tyr204) cellular kit (Cisbio/Perkin Elmer) using the manufacturer’s protocol. Briefly, 10,000 cells/well were plated into a 96-well plate, treated the following day with compound, and incubated for 48 hours. Media were then removed and cells lysed with 50 μL of the provided lysis buffer. Ten μL of lysate was then transferred to a white 384-well plate, the pre-mixed antibody solution added, and the plate was sealed and rocked at room-temperature overnight. The next morning, the plate was read at 620 and 665 nm using the Biotek Cytation 5 plate reader with a TR-FRET red filter cube. The TR-FRET signal is the ratio of 665/620 nm. To normalize for lower overall cell count caused by inhibitor treatment, cells were separately plated and dosed with compound were counted for cell viability using the cell counting protocol described above, and the pERK signal for each treatment was normalized to total cell count for that treatment.

siRNA counterscreen

CRISPR screen hit validation using a plate-based siRNA counterscreen was conducted using pooled on-targetPLUS motifs for indicated proteins from Horizon Discovery. Cells were plated in antibiotic-free medium at 5000 cells/well in a 96-well plate. After 24 hour, medium was replaced with siRNA-containing medium consisting of 0.01% Dharmafect II transfection reagent and 25 nM siRNA sequence specific to the target or non-silencing control (NSC) motif. Control wells received normal media. The CDK4/6 inhibitor palbociclib was added in a 5-point dose-response curve at the same time as siRNA addition. Cells were grown for five days, then viability was read using direct cell counting as described above.

Flow cytometry

Cells were plated in a 6-well dish at 100,000 cells/well. Cells were dosed with compound for five days. Apoptosis assays were performed with the TACS Annexin V–FITC kit (Trevigen) according to the manufacturer’s protocol. For cell-cycle assays, cells were fixed in 70% ethanol for three hours, then stained for three hours at 37°C with 10 μg /mL propidium iodide and 100 μg/mL RNAse in 1x PBS. Cells were analyzed on either a BD LSRFortessa flow cytometer or Beckman Coulter CytoFLEX, and cells were analyzed with FCS Express 7 (De Novo Software).

Immunofluorescence

To analyze pericentrin and γH2AX cells were plated on glass bottom dishes (MatTEK Corporation) and imaged using an EVOS M7000 wide-field microscope with a 63X, 1.4 NA objective. Following inhibitor or vehicle (DMSO) treatment cells were fixed with 4% formaldehyde for 20 min at room temperature, permeabilized with 0.1% Triton for 5 min, rinsed with PBS, and blocked using 3% BSA-PBS for 1 hour at room temperature. The cells were incubated in antibodies targeting pericentrin and γH2AX (1:200 in 3% BSA-PBS) overnight at 4°C or at room temperature for 2 hour, cells were then washed with PBS followed by incubation in Alexa-561 and Alexa-488 secondary antibodies at room temperature for 1 hour. To visualize nuclei, cells were stained with DAPI (1:10,000) for 5-10 min in PBS and subsequently rinsed with PBS. For γH2AX the integrated density per cell was measured and for pericentrin the number of foci were counted per cell, both were done using FIJI. For each cell line a total of 200 cells were analyzed from two independent biological replicates. To calculate the relative intensity of γH2AX all values were divided by the mean of the corresponding vehicle (DMSO) control and then transformed by log2. Pa16C cells stably expressing ERK-KTR-mRuby were plated, treated, fixed, stained for DAPI, and imaged as described above. Images were analyzed using FIJI, the DAPI images were used to outline the nucleus and the ERK-KTR-mRuby images were used to outline the whole cell. The integrated density per nucleus and cell were each measured using the ERK-KTR-mRuby channel. The total cell integrated density was divided by the nuclear integrated density for each cell. A total of 50-150 individual cells were analyzed for each condition. Statistical significance was determined using a one-way ANOVA and Dunnett’s multiple comparison test.

Cells were plated in glass-bottom dishes (MatTEK Corporation) and imaged on a Zeiss 700 confocal microscope with a 63x, 1.4-numerical aperture objective. Cells expressing mCherry-EGFP-LC3 were treated as indicated and imaged live. The 488 and 561 nm laser lines were used to excite EGFP and mCherry, respectively. Images were analyzed in ImageJ, where images were thresholded to isolate mCherry+ and/or EGFP+ autophagic vesicles and autophagic flux was calculated by dividing the total mCherry+ vesicle area to EGFP+ vesicle area.

Data analysis

Raw sequence counts were deconvolved to separate treatment groups from total sequence reads, then sgRNA reads were normalized and analyzed for each gene as previously described (20). We used two methods to pick hits: MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout)-MLE (maximum likelihood estimation) automates sample normalization and comparison between T0, DMSO- and drug-treated samples to determine a beta score, where negative beta score indicate sensitivity to loss of a gene (21), while Redundant siRNA Analysis (RSA) scores genes based on the collective fold-change activity of the entire sgRNA set for each gene and assigns a p-value for statistical significance (22). Using either method, depletion of an sgRNA construct relative to the initial T0 condition for that cell line indicates the loss-of-fitness impact of a gene over time (a measure of gene “dependency”). In contrast, a selective decrease in counts for a gene in the CDK4/6i-treated group compared to vehicle control indicates a CDK4/6i-specific dependency. For analysis using MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout), we ran MAGeCK using MLE with treatment-deconvolved FASTQ files, normalized to control (non-targeting) sgRNA constructs (21,23). To analyze samples using RSA (Redundant siRNA Analysis), the number of counts for each construct was normalized to the total number of counts in the same sample and averaged across both replicates. By comparing the number of cells containing each sgRNA in the palbociclib vs. DMSO-treated cells, we determined the selectivity effect on viability that each sgRNA had on palbociclib co-treatment. Genes were ranked and assigned significance using published RSA analysis software (24). Genes were selected as hits using RSA if multiple sgRNA targeting that gene potentiated drug activity, and a log(p) for that gene, calculated using the RSA method, was ≤ −3 (24).

Heatmaps were built using the R packages pheatmap and ComplexHeatmap (25). The hits were used as input for STRING visualization (26). ORA pathway analysis was conducted using Webgestalt (27).

Pathway analysis

Protein interaction networks were generated using STRING-DB. Associations were drawn using “high confidence” settings. Figures redrawn using Gephi were generated by downloading node and edge data from STRING, importing into Gephi 0.9.2, and drawing using a radial layout arranged by modularity, sorted by degree, and colored by RPPA signal as indicated.

Statistical analysis

Data were analyzed by GraphPad Prism built-in tests (specifics are indicated in figure legends). Data are presented relative to their respective controls. For all graphs, error bars indicate mean or median s.d. for n ≥ 3 independent experiments (except where noted) and P values on graphs are denoted within each figure panel or in the Supplemental Information (RPPA data). Number of samples analyzed per experiment and whether data are the mean of multiple experiments, or selected as representative, are indicated in figure legends.

Druggable genome CRISPR library

The "druggable genome" CRISPR knockout library was described previously (28,29). The pooled library consists of the lentiviral plasmids, each encoding a single guide RNA (sgRNA), the CRISPR-associated nuclease (Cas9) and a gene conferring resistance to puromycin. Five unique sgRNAs for each of the 2,240 genes comprising major cellular and cancer signaling networks, including most of the human kinome, were included in the library. Also included were 50 non-targeting constructs and 750 constructs targeting 150 control genes with previously reported dispensable or essential cellular functions. Library preparation, viral production, and viral titering was as we previously described, except that lentivirus was produced using a mixture of Fugene 6, 12 μg Pax2 packaging plasmid, 3 μg pMD2G envelope plasmid, and 12 μg of the CRISPR library in Optimem.

CRISPR screen

The screen was conducted by first plating 1x10⁶ cells in 2 ml of recommended media per well into 25 six-well plates. The following day, virus and polybrene (8 μg/ml) were added to the cells. Plates were centrifuged at 800 x g for one hour, then incubated overnight at 37°C. The next day, transduction medium was replaced with the fresh medium containing puromycin at 2 μg/ml. As expected, significant cell death developed within two days. All subsequent cell culture was done in presence of puromycin. The remaining cells were trypsinized and re-plated on 500 cm² square culture plates, with 1.2 x 10⁷ cells frozen as a T0 sample. Seven days later, a second sample of 1.2 x 10⁷ cells was frozen (T7 timepoint), and cells were separately plated into drug and replicate treatment groups, to receive continuous treatment with either vehicle (DMSO) control or palbociclib (200 nM) for 4 weeks. Cells were collected after two and four weeks of treatment and flash-frozen in liquid N₂. Two technical replicates for each condition were prepared, excepting Pa16C where only a single replicate was collected. MIA PaCa-2 cells were collected only at two weeks of treatment. Cells were always maintained at no fewer than 1.2 x 10⁷ cells to maintain at least 1000x coverage of the library in the population. Genomic DNA was isolated and prepared for sequencing as previously described (20). Sequencing was performed on an Illumina NextSeq 500 with 75bp, single-end reads. A final concentration of three pmol DNA was loaded with a PhiX spike of greater than 15%.

Data Availability

The data generated in this study are available within the article and its supplementary data files or upon request from the corresponding author.

Results

CDK4/6 inhibitors reduce PDAC cell viability and induce compensatory signaling changes

Given the lack of clinical efficacy seen with single agent CDK4/6 inhibitor treatment in PDAC (30), we sought to identify inhibitor combination strategies to enhance their anti-tumor activity. First, we established a baseline of CDK4/6 dependency and addressed the relative importance of the two highly related CDK4 and CDK6 kinases in PDAC cell lines. Interestingly, we found that acute (72 hours) siRNA suppression of either CDK4 or CDK6 alone in some cell lines caused a compensatory increase in the expression of the other protein (Fig. 1A). We found that while single gene suppression did not cause significant reductions in growth, concurrent CDK4/6 suppression was much more effective (Fig. 1B). Our findings agree with data from Project Achilles (DepMap), where a panel of 25 PDAC cell lines showed little dependence on CDK4 or CDK6 for proliferation after RNAi-mediated knockdown (Supplementary Fig. S1A). These findings are also consistent with other studies that support the functionally redundant activities of CDK4 and CDK6 (11,31). All clinically approved inhibitors targeting CDK4 and CDK6 (palbociclib, ribociclib, and abemaciclib), as well as the clinical candidate lerociclib, have dual activity against both kinases, with abemaciclib also inhibiting several transcriptional CDKs including CDK9 (32).

Figure 1.

CDK4/6 inhibition reduces PDAC cell viability and induces compensatory signaling changes. A, Representative western blots for indicated proteins after 72 hours treatment with siRNA targeting CDK4, CDK6, the combination, or non-silencing control (NS). Protein expression ratio to NS indicated. B, Cell proliferation after siRNA-based silencing of protein expression in indicated cell lines. Cell proliferation was measured by automated direct cell counting in a 96-well format 5 days post-knockdown (n ≥ 3, error bars SD). C, Average EC₅₀ (nM) for palbociclib in the indicated PDAC cell lines in an anchorage-dependent proliferation assay. D, Top 25 largest fold increases (red) or decreases (blue) in protein phosphorylation or expression compared to vehicle control upon treatment with 200 nM palbociclib for 5 days, as measured using RPPA in six cell lines (full dataset in Table S1). E, Dot-plot of values for each cell line from panel D for indicated proteins and pathways. Activating ERK phosphorylation at T202/204 was the largest upregulation in response to CDK4/6i treatment in the dataset. F, Microscopy images (40x) of Pa16C cells constitutively expressing the mCherry-ERK-KTR treated with 400 nM palbociclib or DMSO. Higher ERK activity causes nuclear export of the ERK-KTR reporter, thus leading to higher cytoplasmic/nuclear fluorescence ratio. Scale bar = 60 μm. G, Palbociclib causes significantly higher ERK activity compared to vehicle. ERK inhibitor SCH772984 at 200 and 1000 nM shown as a positive control (error bars SEM).

Since CDK4/6 inhibitors have been reported to increase oxidative cellular metabolism through on-target mechanisms (14,33), we first determined whether these activities would confound metabolism-based cell viability assays. We found that metabolism-based viability assays (MTT, alamar blue and ATPLite) dramatically underestimated the anti-proliferative activity of the CDK4/6 inhibitor palbociclib (but not the ERK1/2-selective inhibitor SCH772984) compared to plate-based automated direct cell counting, presumably due to the palbociclib-induced upregulation of mitochondrial respiration and ATP production (Supplementary Fig. S1B) (14). Therefore, we used direct cell counting in all viability assays to avoid falsely underestimating CDK4/6 inhibitor activity.

We found that 13 of 15 PDAC cell lines were sensitive to palbociclib, the first clinically approved CDK4/6 inhibitor, in an anchorage-dependent proliferation assay (Fig. 1C, Supplementary Fig. S1C). PDAC cells were sensitive to palbociclib and other approved CDK4/6 inhibitors in anchorage-dependent (Supplementary Fig. S1D, S1E), and anchorage-independent clonogenic colony growth formation (Supplementary Fig. S1F, S1G). Palbociclib reduced the level of phosphorylated (inactivated) RB (pRB), a well-validated marker for CDK4/6 inhibition, at an IC₅₀ of approximately 100 nM (Supplementary Fig. S1H), comparable to what has been described previously (34). As expected, CDK4/6 inhibition induced a strong G1 arrest, with treatment increasing the G1 population by 80% (Supplementary Fig. S1I). The strong similarity in mechanistic and phenotypic activity seen with chemically distinct CDK4/6 inhibitors suggests on-target growth suppression.

Strong short-term efficacy of protein kinase inhibitors is frequently limited by long-term treatment-induced kinome reprogramming and the onset of resistance mechanisms (35,36). For example, while RB phosphorylation was strongly inhibited at 24 hours of CDK4/6i treatment, pRB returned to pretreatment levels by 3 to 5 days (Supplementary Fig. S1J). To identify further compensatory signaling changes caused by CDK4/6 inhibition, we treated six PDAC cell lines with palbociclib for 5 days and applied reverse phase protein array (RPPA) analyses to monitor alterations in protein phosphorylation or expression of cancer signaling proteins (37). As anticipated given the genetic heterogeneity of PDAC (5), there was considerable cell line variation in signaling activities detected (Supplementary Fig. S1K, S1L). However, the six cell lines responded similarly to palbociclib overall (Fig. 1D). CDK4/6 inhibition consistently reduced pRB and the cell proliferation marker Ki-67, as expected. Palbociclib also induced strong compensatory modulation of key signaling pathways, including upregulation of proteins in the anti-apoptotic and PI3K-AKT-mTOR pro-survival signaling pathway (Fig. 1E). Surprisingly, the single largest upregulation was increased phosphorylation and activation of ERK (pERK), indicating dramatically increased upstream pro-proliferative signaling through the ERK MAPK pathway. We further validated treatment-induced ERK activation using the ectopically expressed ERK Kinase Translocation Reporter (KTR), which consists of the ERK substrate recognition motif from ELK1 tagged with mRuby2 red fluorescent protein (38). We observed significantly higher ERK activity after CDK4/6i treatment compared to vehicle control, as indicated by higher cytoplasmic levels of the ERK-KTR fluorescence (Fig. 1F, G; Supplementary Fig. S1M). To confirm this finding, we measured pERK activity after palbociclib, abemaciclib, or ribociclib treatment using a TR-FRET assay and saw a strong dose-dependent increase in pERK levels (Supplementary Fig. S1N). In totality, we found that three chemically distinct CDK4/6 inhibitors increase pERK across a panel of six cell lines, as assayed using RPPA, pERK TR-FRET, western blots, and an ERK activity reporter cell line. Multiple lines of evidence therefore suggest that CDK4/6i-driven ERK activation is a major mechanism by which cancer cells compensate to counter CDK4/6 suppression and the concomitant anti-proliferative signaling.

CDK4/6 inhibitors synergize with ERK MAPK inhibitors to target PDAC

Because CDK4/6i treatment induced compensatory pro-proliferative ERK upregulation, we reasoned that combined therapy would cooperatively block PDAC cell growth. We found that concurrent treatment with the ERK1/2-selective inhibitor SCH772984 (ERKi) and CDK4/6i synergistically reduced the proliferation of PDAC cell lines in anchorage-dependent (Fig. 2A-C) and colony formation assays (Fig. 2D; Supplementary Fig. S2A). Lower concentrations of ERKi and CDK4/6i were required to cause the same loss of proliferation when used in combination than when used separately, and combined treatment sensitized PDAC lines that were initially resistant to CDK4/6i (e.g., MIA PaCa-2). Similar results were seen with the combination of lerociclib with SCH772984 (Supplementary Fig. S2B) or with a second ERK1/2-selective inhibitor (ulixertinib/BVD-523) together with CDK4/6i (Supplementary Fig. S2C). Recent studies determined that concurrent CDK4/6 inhibition enhanced the activity of direct KRAS G12C mutant-selective inhibitors (39). We found that KRAS^G12C-mutant MIA PaCa-2 cells were resistant to CDK4/6i, but concurrent treatment with the KRAS^G12C-mutant selective inhibitor sotorasib/AMG510 synergistically suppressed MIA PaCa-2 growth (Supplementary Fig. S2D, S2E).

Figure 2.

CDK4/6 inhibitors synergize with ERK inhibitors to reduce viability and overcome CDK4/6 inhibitor-mediated signaling compensatory changes. A, Cell viability dose-response curves for ERK inhibitor SCH772984 in combination with fixed indicated concentrations of palbociclib under anchorage-dependent cell culture conditions (representative of at least three independent experiments). B, Three-dimensional representation of (A), color-coded with BLISS synergy scores (red = synergy, blue = antagonism). C, Maximum BLISS synergy scores across a panel of PDAC cell lines (BLISS <1 is synergistic). D, Fourteen-day colony formation assays for the palbociclib and SCH772984 combination (representative of two independent experiments). E, Western blot of cells treated with palbociclib alone or combined with SCH772984 after three days treatment (representative of two independent experiments). pRB expression ratio to Tubulin is indicated. F, Western blot for RB phosphorylation with one to five days treatment of palbociclib, SCH772984, or the combination over a wide dosing range (representative of two independent experiments).

Treatment with ERKi or CDK4/6i alone caused a partial reduction in pRB, but combined treatment caused more complete loss of pRB and reduction of phosphorylation of the ERK cytoplasmic substrate p90RSK (pRSK) (Fig. 2E). These results were phenocopied by siRNA silencing of CDK4 and CDK6 concurrently with ERKi treatment (Supplementary Fig. S2F). Notably, while treatment with CDK4/6i alone caused a transient reduction in pRB, concurrent ERKi and CDK4/6i treatment caused sustained reduction in pRB (Fig. 2F). Taken together, our results strongly suggest concurrent CDK4/6i and ERKi synergistically decrease viability in PDAC.

CDK4/6i and ERKi synergistically suppress G1 progression, induce senescence and apoptosis, and suppress compensatory signaling

We next determined how concurrent CDK4/6 and ERK inhibition synergistically reduced cell viability. Combined CDK4/6i or ERKi induced G1 cell cycle arrest as anticipated based on the strong arrest caused by each agent individually (40,41) (Supplementary Fig. S2G and H). Both palbociclib single-agent and combination treatment with ERKi induced senescence (Supplementary Fig. S2I-S2K). However, while ERKi or CDK4/6i treatment alone caused limited apoptosis, the combination markedly increased apoptosis in the majority of cell lines tested (Fig. 3A and B, Supplementary Fig S3A).

Figure 3.

Combination of CDK4/6i and ERKi alters cell cycle regulation, increases apoptosis, and induces compensatory cellular signaling. A, Flow cytometry-based annexin-V/PI staining for apoptosis in MIA PaCa-2 cells induced by CDK4/6i palbociclib (600 nM), ERKi SCH772984 (500 nM), or the combination after five days of treatment (representative images of three independent experiments). B, Quantified percent apoptosis after palbociclib, SCH772984, or the combination at two palbociclib concentrations across a PDAC panel (error bars, SD of at least two independent experiments) from flow cytometry analyses (panel A). C, Log2 fold-change RPPA results after treatment with CDK4/6i, ERKi, or the combination for 14 days across six PDAC lines. Shown are the top and bottom 20% quantile (upregulation, red or downregulation, blue) to combination treatment. D, Log₂ fold-change (FC) for RB (S780) phosphorylation and E, MYC expression upon CDK4/6i, ERKi, or combination treatment over a time course of one to 14 days.

We next measured a system-wide profile of temporal signaling changes caused by single and combination inhibitor treatment using RPPA analyses by measuring total and phosphoprotein levels at times ranging from one to 14 days. We found that signaling responses to single-agent CDK4/6i or ERKi treatment differed strongly at 24 and 72 hours (Supplementary Fig. S3B, Supplementary Table S1). Whereas ERKi caused both down- and up-regulation of protein signaling activities that were consistent over time, 24-hour treatment with CDK4/6i was almost exclusively inhibitory and not correlated to the response in ERKi-treated cells. However, after three days, a significant fraction of signaling in CDK4/6i-treated cells was reactivated, and by day 14, CDK4/6i and ERKi signaling compensations were highly correlated (Fig. 3C; Supplementary Fig. S3C). The correlated long-term response was not an indirect consequence of G1 arrest since both ERKi and CDK4/6i cause G1 arrest by 24 hours (Supplementary Fig. S1I). In contrast, CDK4/6i-driven signaling changes are highly correlated with CDK4/6i + ERKi treatment at all timepoints (Supplementary Fig. S3D), suggesting that CDK4/6i effects supersede those caused by ERKi. Ultimately, the cellular response at longer timepoints to all three inhibitors were largely similar, though the magnitude of the response to combination treatment was generally greater than that seen with single agent treatment.

Kinome alteration extended far beyond the cell cycle regulatory proteins (Supplementary Figure S3E). In particular, RTK-mediated activation of RAS effector signaling increased sharply after five and 14 days of CDK4/6i treatment, as did activation of the PI3K-AKT-mTOR-S6 kinase signaling axis, and AMPK, PAK1, and IGF1R protein kinases. These results are consistent with our previous finding that ERKi-mediated changes involved not only downregulation of signaling activities that drive growth suppression but also upregulation of receptor tyrosine kinase (RTK)-mediated compensatory signaling changes (29,42). Interestingly, the 5-day response to CDK4/6i, ERKi, or the combination resembled the signaling alterations caused by acute siRNA knockdown of KRAS (72 hours) (Supplementary Fig. S3F) (43). Thus, the direct consequences of KRAS suppression were phenocopied by concurrent inhibition of ERK and CDK4/6.

The combination of CDK4/6i and ERKi suppressed signaling activity more strongly than either inhibitor alone. RB dephosphorylation and activation was decreased more substantially by the combination, as were cell cycle activating proteins (e.g., CDK1, CDK2, cyclin A and B1, Aurora A, FOXM1) (Fig. 3C and 3D). ERK activity itself, as measured by phosphorylated ERK protein or ERK substrate RSK, was blocked by the combination despite increasing with CDK4/6i alone (Supplementary Fig. S3G).

We recently showed that MYC loss was essential for the anti-tumor activity of ERKi in PDAC, and that direct MYC suppression impaired PDAC tumorigenic growth (42). Whereas CDK4/6i treatment alone only weakly reduced MYC protein levels, concurrent treatment with CDK4/6i and ERKi strongly ablated MYC protein levels (Fig. 3E). Thus, potent MYC reduction may be a key basis for the synergistic growth suppression caused by the combination. To address this possibility, we performed a rescue experiment by ectopic overexpression of MYC and measured cell viability upon treatment with CDK4/6i and ERKi in a dose-response matrix. Overexpression of MYC antagonized the response to CDK4/6i, ERKi, or combination treatment (Supplementary Fig. S3H, I), supporting a role for MYC loss in the growth suppressing activity of these inhibitors. In MIA PaCa-2 cells, the EC₅₀ for CDK4/6i alone or in combination with ERKi increased over 10-fold compared with empty vector (EV) control, while the single-agent ERKi EC₅₀ increased two-fold.

Combined treatment with CDK4/6i and ERKi reduces organoid cell proliferation and induces apoptosis

We next evaluated the combination in KRAS-mutant PDAC patient-derived organoid models that may better model patient response (44). Organoid cell viability was synergistically reduced by the combination (Fig. 4A-C; Supplementary Fig. S4A and S4B), and apoptosis was increased as determined by caspase activation (Fig. 4D). The combination also synergistically suppressed the growth of KRAS-mutant colorectal cancer organoids (Supplementary Fig. S4C-H), suggesting that the effectiveness of this combination is not limited to PDAC.

Figure 4.

Combined CDK4/6i and ERKi inhibition induces apoptosis in PDAC organoids. A, PDAC patient-derived hT2 organoids treated with indicated concentrations of palbociclib and ERK inhibitor SCH772984 imaged at 4x resolution (representative images of three independent experiments, scale bar = 500μm). B, Organoid (hT2 and hM1a) viability as measured by CellTiter-Glo (Promega) treated with SCH772984 and palbociclib in a dose-response matrix (representative of three independent experiments). C, Three-dimensional representation of the luminescence (viability) scores from (A), colored by BLISS synergy (red) or antagonism (blue) between CDK4/6i and ERKi in hT2 and hM1a organoids. D, Induction of caspase activity as measured by Caspase-Glo (Promega) in organoid models treated with palbociclib and SCH772984 (representative of two independent experiments).

Loss-of-function CRISPR/Cas9 screen identifies synergistic and antagonistic interactions with CDK4/6i

Most clinical and pre-clinical evidence suggests that CDK4/6 inhibitors are substantially more effective in combination with other targeted therapies (45). To identify additional combinations that have not been described previously, we applied a CRISPR-Cas9 loss-of-function screen to identify genes whose loss sensitized cells to CDK4/6i treatment and therefore represent potential combination strategies with CDK4/6i. We applied a focused library targeting 2,240 genes that comprise major cellular and oncogenic signaling networks (Fig. 5A; Supplementary Fig. S5A) (28,29). We screened six KRAS-mutant PDAC cell lines treated with a sublethal dose (GI₃₀ or below) of CDK4/6i (Fig. 5B; Supplementary Fig. S5B). Similar timepoints, treatment groups, and cell lines clustered together by using principal component analysis (Supplementary Fig. S5C and S5D) and in a Pearson correlation matrix (Supplementary Fig. S5E). Individual genes were scored for their effect on proliferation using MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout)-MLE (maximum likelihood estimation), where negative beta scores indicate sensitivity to loss of a gene (21). Essential and non-essential genes closely agreed with prior studies with excellent precision/recall (Supplementary Fig. S5F) (46).

Figure 5.

Loss-of-function CRISPR-Cas9 screen discovers CDK4/6i-sensitizing genes. A, Composition of a loss-of-function CRISPR “druggable genome” library targeting 2,240 genes. B, Screening workflow: cells were infected with lentivirus expressing Cas9 and barcoded sgRNA constructs in a pooled format, with an initial sample collected after transfection to measure library representation (T0). Cells were grown for an additional seven days to for DNA alteration and initial selection of early essential genes (sample T7 collected). Cells were then split into two replicates of palbociclib- or control-treatment arms and dosed continually (200 nM) for two or four weeks. C, Average difference beta score across all six cell lines and both timepoints. Negative values mean loss of indicated protein enhances CDK4/6 inhibitor palbociclib sensitivity. D, Top 50 genes with the most palbociclib-selective difference beta scores by median across all cell lines and timepoints using MAGeCK-MLE. E, Average RSA sensitivity LogP score across all six cell lines and both timepoints. Negative values mean loss of indicated proteins enhance palbociclib sensitivity.

We evaluated which gene knockouts selectively affected cell proliferation in combination with two or four weeks of CDK4/6i at sublethal concentrations. As expected, loss of RB1 potently antagonized CDK4/6i growth suppressive activity in all cell lines (Supplemental Fig. S5G-S5I). Because our PDAC cell lines are very heterogeneous, we averaged the selectivity scores across the six cell lines at both timepoints in order to find the consensus sensitizing hits (Fig. 5C, D; Supplemental Table S2 and S3). To increase confidence in our hits, we separately picked hits using Redundant siRNA Analysis (22), an orthogonal approach that largely agreed with MAGeCK to identify genes that enhanced growth suppression in a CDK4/6i-selective manner (Fig. 5E, Supplemental Fig. 5J; Supplemental Table S4).

As expected, there was significant heterogeneity among the six cell lines, even among the 50 most CDK4/6i-sensitizing proteins (Fig. 5D). These genes encode proteins centered around diverse signaling nodes including PI3K-AKT-mTOR signaling, cell cycle regulation and mitosis, SRC family kinase signaling, cell metabolism and biosynthesis, chromosome regulation and maintenance, and DNA damage and repair pathways. Genes related to metabolism and DNA damage were highly enriched. Several hits are known to reduce CDK4/6 activity when lost, such as RUNX1 (47) and HDACs (48), immediately suggesting a mechanistic basis for their identification.

Our RPPA analysis found that CDK4/6 inhibition resulted in activation of signaling proteins with known driver functions in cancer, including PAKs and components of the PI3K-AKT pathway. That these pathways were also found as sensitizing CRISPR screen hits further emphasizes their importance as compensatory mechanisms to overcome loss of CDK4/6 function. Using a random walk network propagation analysis on CDK4/6i-sensitizing hits based on the PPI-BIOGRID database(27,49), we found that HDAC2 and MYC displayed the highest random walk probability of the MAGeCK-MLE and CRISPR hits, respectively, followed closely by epigenetic regulators, DNA-damage response genes, and CDK2 (Supplemental Fig. S5K-SM). Suppression of MYC is a mechanistic basis for the synergistic growth inhibitory activity caused by concurrent suppression of CDK4/6 and ERK (Fig. 3E), and supporting this mechanism, genes that regulate MYC expression were also identified in the CRISPR screen. These included the tumor suppressor FBXW7, which encodes an E3 ligase that directly causes MYC protein degradation driven through the ERK-regulated phosphodegron sequence (50).

We also identified genes whose loss imparted a survival advantage under CDK4/6i treatment, which as expected includes RB1 (Supplementary Fig. S5I, S5N, S5O). Loss of many of these genes have an anti-proliferative effect by themselves (e.g., CDK1, CHEK2), but the anti-proliferative effect in combination with CDK4/6i was less than additive.

Genes whose loss increased sensitivity to CDK4/6i were broadly involved in regulation of RNA Pol II transcription, the cell cycle, and DNA damage (Supplementary Fig. S6A). To validate individual sensitizing genes, we applied siRNA oligos, whose acute and partial suppression of expression better mimics the partial inhibition seen with pharmacologic inhibitors (pooled with four constructs per gene). Consistent with their identification in the CRISPR screen, suppression of these genes caused a sensitizing and frequently synergistic shift in the CDK4/6i EC₅₀ in one or more cell lines (Fig. 6A). For example, suppression of CDK2, BAP1 or EZF3 expression synergistically increased sensitivity to CDK4/6i (Fig. 6B). We observed synergism between BAP1 loss and CDK4/6 inhibition in our CRISPR screen and validation (Fig. 6A and 6B, Supplementary Fig. S6B and S6C). The deubiquitylase BAP1 is classically (though not exclusively) described as a tumor suppressor driving cancer progression when lost through germline or somatic loss-of-function mutations (51). However, in this context our results support a pro-growth function for BAP1 in pancreatic cancer and a synergistic anti-proliferative effect with CDK4/6i.

Figure 6.

Palbociclib-sensitizing CRISPR hits validated using siRNA counterscreen and small-molecule inhibitors. A, Indicated gene hits from the CRISPR screen were individually silenced using siRNA, then treated with palbociclib. Shown is the fold-shift in the EC₅₀ of palbociclib compared to cells treated with non-specific siRNA (red = more sensitive, blue = less sensitive). Data is shown as average of three independent replicates. B, Example of data used to generate (A). Dose-response curves were measured for palbociclib in siRNA-treated cells, and cell viability was measured using direct cell counting. BLISS synergy score was calculated for each combination (red = synergy). C, Indicated compounds were tested against the panel of PDAC lines used in the CRISPR screen in a dose-response matrix with palbociclib; on average, drugs were synergistic (red), antagonistic (blue), or additive (white). D-E, Example matrix dose-response curves for SRC family inhibitor bosutinib (Pa02C) and HDAC inhibitor quisinostat (Pa16C) at different fixed concentrations of CDK4/6 inhibitor palbociclib (upper panel), with BLISS synergy shown mapped on the 3D dose-response matrix (lower panel).

To further validate the screen, we evaluated small molecule inhibitors of some of the hits and determined that many synergistically decreased cell proliferation with CDK46i (red, Fig 6C). For example, the SRC inhibitor bosutinib and HDAC inhibitor quisinostat synergistically increased CDK4/6i activity (Fig. 6D and 6E). Some of the combinations were either additive (white) or less-than-additive (blue), though all combinations resulted in greater overall reduction in viability as compared to single-agent treatment in at least one cell line.

CDK4/6 inhibition increases sensitivity to autophagy inhibition

Autophagy, a tightly regulated process whereby cells break down intracellular components, is often upregulated as a stress-tolerance response (52). It has been reported that CDK4/6 inhibition promoted compensatory upregulation of autophagy (53), and we recently determined that autophagy is elevated in response to ERK inhibitor-mediated suppression of glycolysis, resulting in enhanced sensitivity to autophagy inhibition (19). Interestingly, our CRISPR screen identified multiple genes that encode autophagy-blocking proteins as sensitizers to CDK4/6i, including CSNK2A/B (CK2) and PHF23. We used a fluorescence-based reporter to confirm that CDK4/6 inhibition caused increased autophagic flux (Supplementary Fig. S6D and S6E), supporting the conclusion that elevated autophagy upon CDK4/6i is an adaptive response to promote PDAC cell survival. Providing a mechanistic basis for CDK4/6i-induced autophagy, our RPPA dataset identified phosphorylation and activation of AMPK by combination CDK4/6i and ERKi treatment (Fig. 3C). AMPK activation is a key initiating step in the induction of the autophagy pathway (52). To exploit the role of autophagy as a compensatory and protective response to CDK4/6i treatment, we evaluated concurrent treatment with CDK4/6i and the autophagy inhibitor chloroquine (CQ) and observed synergistic loss of viability after five-day treatment (Supplementary Fig. S6F and S6G).

The effects of CDK4/6i are strongly enhanced by combination with CDK2 inhibition

CDK2 was the strongest hit from the CRISPR screen (Fig. 5D, 6A and 6B, Supplementary Fig. S5K). CDK2 is activated by cyclin E, the cyclin E-CDK2 complex cooperates with cyclin D-CDK4/6 to promote G1 progression, and CDK2 can partially compensate for CDK4/6 loss to promote G1/S transition (31). CDK2 additionally helps maintain cells in S phase, regulates apoptosis by stabilizing anti-apoptotic MCL-1 levels, activates AKT through direct phosphorylation, and of particular importance, replicates ERK activity by stabilizing MYC through direct phosphorylation on S62 (54). Each of these roles would support concurrent CDK2 inhibition in combination with CDK4/6i. To address this possibility, we utilized the potent and selective CDK2/4/6 inhibitor (CDK2/4/6i) clinical candidate PF-06873600 (55). We found that CDK4/6i and CDK2/4/6i showed comparable EC₅₀ concentrations in blocking cell proliferation in anchorage-dependent viability assays (Supplementary Fig. S7A). However, they caused distinct consequences on cell cycle progression. While CDK4/6i caused G1 arrest, CDK2/4/6i treatment caused minimal increase in cells in G1 and instead, the percent of cells in S phase increased from 27% to 36% on average (Fig. 7A). Furthermore, CDK2/4/6i induced levels of apoptosis between 3.5- and 8.7-fold higher than CDK4/6i (Fig. 7B). CDK2/4/6 inhibition also caused a much larger increase in DNA damage than CDK4/6i (Supplementary Fig. S7B and S7C), and caused strong increases in polycentrism, leading to anaphase collapse and apoptosis (Supplementary Fig. S7B and S7D).

Figure 7.

CDK2 inhibition synergizes with CDK4/6i by cooperatively inhibiting cell cycle proteins and MYC. A, Violin plot of cell cycle distribution for the six PDAC cell lines used in the CRISPR screen and treated with DMSO, palbociclib, or CDK2/4/6 inhibitor PF-06873600 (each at 300 nM) (representative of three independent experiments). B, Percent apoptotic cells after treatment with palbociclib or CDK2/4/6 inhibitor PF-06873600 (200 nM) for five days (error bars, SD of at least three independent experiments). C, RPPA results after 14 days PF-06873600 treatment showing increased (red, log2 fold-change) or decreased protein (blue). D-E, Change in indicated proteins over one, three, and 14 days with PF-06873600. F, ERK activation by PF-06873600 as measured using the ERK-KTR reporter readout. G, Viability dose-response curves for response matrix in PDAC-derived organoids treated with PF-06873600 plus ERKi for five days (representative of three independent experiments).

Since CDK2/4/6i and CDK4/6i treatments caused different phenotypic consequences, we applied RPPA analyses to determine system-wide inhibitor-induced temporal changes in signaling protein activation or expression. CDK2/4/6i sharply downregulated protein expression and phosphorylation in a time-dependent manner, with the strongest effect seen after 14 days (Fig. 7C; Supplementary Fig. S7E). Proliferation and mitosis markers were strongly inhibited (Ki67 and histone H3 pS10), and CDK2/4/6i more potently suppressed MYC expression than did CDK4/6i (Fig. 7D). RB and cyclins A2, B1, D2, E1, and E2 were strongly blocked (Fig. 7E). As we saw with CDK4/6i, CDK2/4/6i treatment also caused a compensatory increase in ERK activation (Fig. 7F, Supplementary Fig. S7F) which strongly suggested that a CDK2/4/6 combination might also synergize with ERKi. Indeed, combining CDK2/4/6i with ERKi blocked pERK activation and further reduced cell viability in cell lines (Supplementary Fig. S7G) and PDAC patient-derived organoids, with strong or partial synergy observed in each of the six organoid models tested (Fig. 7G; Supplementary Fig. S7H). Finally, to assess the on-target activity of CDK2/4/6i, we used siRNA to silence CDK2 and observed that the combination of CDK2 siRNA and CDK4/6i led to a cooperative dose-dependent decrease in anti-proliferative activity as well as to increased apoptosis compared to either alone (Supplementary Fig. S7I-K). Thus, concurrent CDK2 suppression together with CDK4/6i treatment phenocopied the activities seen with CDK2/4/6i.

Discussion

Despite the well-validated role of CDKN2A tumor suppressor loss in PDAC development, pharmacologic restoration of CDKN2A function with CDK4/6 protein kinase inhibitors has shown limited clinical efficacy in PDAC (30). To identify combination strategies to enhance CDK4/6 inhibitor activity in KRAS-mutant PDAC, we applied RPPA to profile CDK4/6 inhibition-induced signaling changes that may drive de novo resistance, and we performed a CRISPR-based genetic loss-of-function screen to identify genes that modulate PDAC sensitivity to CDK4/6 inhibition. Together, these analyses identified multiple combinations that synergistically enhanced CDK4/6 inhibitor activity in preclinical models of KRAS-mutant PDAC and KRAS-mutant colorectal cancer. Our CRISPR screen expanded the field of known synergistic combinations with CDK4/6i, with the highest enrichment consisting of RAS pathway proteins (including ERK2), and encompassing metabolic glycolysis pathway; DNA synthesis, replication, and damage pathways; other cell-cycle regulatory proteins; anti-apoptotic proteins; and epigenetic regulators. The broad range of these hits suggests that CDK4/6i combinations may have clinical utility outside of the limited range of combinations currently evaluated.

A critical limitation seen with essentially all protein kinase inhibitors involves treatment-induced compensatory signaling activities that drive de novo and acquired resistance (35,36). Our system-wide analyses of signaling changes in response to prolonged CDK4/6 inhibition determined that KRAS-mutant PDAC cells attempt to overcome the deleterious consequences of CDK4/6 suppression by upregulating several key oncogenic pathways, including anti-apoptotic proteins and the PI3K-AKT-mTOR and ERK MAPK RAS effector signaling networks. These findings were conducted in anchorage-dependent in vitro cellular systems. It is probable that a similar study conducted in a tumor model with intact tumor and stromal microenvironment (TME) would identify additional inhibitor bypass mechanisms given the strong role the TME plays in driving resistance, as has been shown specifically for CDK4/6 inhibition in other contexts (56)

The rational combination of CDK4/6i with ERKi to block this compensatory activation was effective in both KRAS-mutant PDAC and colorectal organoids. Interestingly, whereas the combination with MEKi showed enhanced induction of senescence but not apoptosis, we found that concurrent CDK4/6i and ERKi treatment showed strong induction of apoptosis and a lower required concentration of each inhibitor in combination to elicit the same loss of viability seen when used as a single-agent. This difference may reflect the refractoriness of ERKi to ERK reactivation mediated through loss of ERK-dependent negative feedback inhibitory mechanisms (57).

Our findings provide the rationale for our initiation of a phase I trial evaluating the ERK inhibitor ulixertinib in combination with palbociclib in patients with metastatic pancreatic cancer (NCT03454035). Our observations agree with other studies that determined a similar enhancement of CDK4/6i activity when combined with MEK inhibitor (MEKi) treatment (10,15) in PDAC, providing the rationale for a Phase I perioperative analysis of the MEKi binimetinib and palbociclib in KRAS-mutant PDAC and other cancers (NCT04870034).

RAS-addicted PDAC cells are dependent on autophagy for survival, in particular when the RAS-MAPK pathway is inhibited, and blockade of autophagy is synergistically toxic in this context (19). CDK4/6i has been reported to increase autophagy, so we were not surprised that hits from the CRISPR screen with CDK4/6i block autophagy. We tested this hypothesis with the general autophagy inhibitor hydroxychloroquine (HCQ) and found strong synergy with CDK4/6i to block growth.

From our CRISPR screen, our most consistent sensitizer to CDK4/6i was CDK2, a kinase that can compensate for CDK4/6 loss to promote G1/S transition and is also important in regulating S-phase transition, cell division, and other pathways (31). Comparing the activities of a CDK2/4/6 inhibitor to CDK4/6 inhibition alone, we observed significantly greater induction of apoptotic death and DNA damage upon treatment with CDK2/4/6i, supporting an enhanced therapeutic potency of concurrent CDK2 inhibition. However, as with CDK4/6i, ERK reactivation remained a limitation for CDK2/4/6i, and concurrent ERK inhibition to block this shared compensatory response further enhanced CDK2/4/6i growth inhibitory activity. We also determined that MYC loss provided one basis for the more potent growth suppression seen with CDK2/4/6i that was further enhanced with concurrent ERK inhibition. The importance of MYC loss for combination inhibitor efficacy is supported by a recent determination that MYC is a driver of resistance to CDK4/6 inhibition (55).

During the course of completion of our studies, two studies reported that concurrent CDK2 inhibition enhanced CDK4/6 inhibitor anti-tumor activity in vitro and in vivo. First, Knudsen and colleagues reported that concurrent CDK2 and CDK4/6 inhibition was more effective in pancreatic cancer due to more efficient restoration of RB activity (58). Second, Freeman-Cook et al. determined that CDK2 was a significant driver of resistance to CDK4/6 inhibition, leading to their development of PF-06873600 for concurrent inhibition of CDK2/4/6 (55). They showed that PF-06873600 was active in a large panel of patient-derived PDAC tumors. However, both studies found that CDK2/4/6i caused a greater induction of senescence compared with CDK4/6i, whereas we observed a more potent apoptotic activity with triple CDK inhibition. The basis for the different findings is not clear.

We identified loss of MYC expression as one mechanistic basis for the potent growth suppressive activity of combined CDK4/6 and ERK inhibition. We determined previously that PDAC sensitivity to ERK inhibition correlated with proteasomal degradation of MYC (41,42). We found that whereas CDK4/6i alone did not reduce MYC expression, it caused a synergistic decrease in MYC when combined with ERKi treatment. Thus, as we have described with other ERK inhibitor combinations, despite the highly divergent targets of the combined inhibitors (tubulin, SRC tyrosine and CHK1 serine/threonine kinases), they converged on driving loss of MYC (59,60). This consistent role of MYC in diverse ERK inhibitor combinations reflects the key role of MYC in ERK-dependent PDAC growth (29,42).

In summary, our application of unbiased systemwide signaling and genetic loss-of-function screens identified a diverse spectrum of functionally distinct signaling components that antagonize CDK4/6i efficacy in PDAC. These findings led us to identify and validate inhibitor combinations that enhance CDK4/6 inhibitor cytotoxicity. Among these combinations, concurrent inhibition of CDK2 and ERK represents a logical and promising combination. Currently, the triple CDK2/4/6 inhibitor PF-06873600 is under phase I/IIa clinical evaluation in hormone receptor-positive, HER2-negative breast and ovarian cancer. Our results, together with those of two recent studies (55,58), support the clinical evaluation of CDK2 inhibitors in combination with CDK4/6i for KRAS-mutant PDAC.

Supplementary Material

22

Supplementary Table 1 – RPPA results for ERKi, CDK4/6i, and combination treatment

Supplementary Table 2 – MAGeCK-MLE results for CRISPR screen

Supplementary Table 3 – Top 100 most CDK4/6i-selective hits, by median beta score using MAGeCK-MLE

Supplementary Table 4 – CRISPR screen RSA results for CDK4/6i sensitizers

Significance.

CRISPR-Cas9 screening and protein activity mapping reveal combinations that increase potency of CDK4/6 inhibitors and overcome drug-induced compensations in pancreatic cancer.