Tumor formation is a multistep process during which cells acquire genetic and epigenetic changes until they reach a fully transformed state. We show that CDK6 contributes to tumor formation by regulating transcriptional responses in a stage-specific manner. In early stages, the CDK6 kinase induces a complex transcriptional program to block p53 in hematopoietic cells. Cells lacking CDK6 kinase function are required to mutate TP53 (encoding p53) to achieve a fully transformed immortalized state. CDK6 binds to the promoters of genes including the p53 antagonists Prmt5, Ppm1d, and Mdm4. The findings are relevant to human patients: Tumors with low levels of CDK6 have mutations in TP53 significantly more often than expected.
Significance: CDK6 acts at the interface of p53 and RB by driving cell-cycle progression and antagonizing stress responses. While sensitizing cells to p53-induced cell death, specific inhibition of CDK6 kinase activity may provoke the outgrowth of p53-mutant clones from premalignant cells. Cancer Discov; 8(7); 884–97. ©2018 AACR.
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The cell-cycle kinase CDK6 has attracted considerable attention over the past decade. CDK6 has been recognized not only as a typical cyclin-dependent kinase but as a transcriptional regulator with properties distinct from those of its close homolog CDK4 (1–5). CDK6 regulates the transcription of a number of genes, and its effects may be dependent on or independent of its kinase activity (6). The transcriptional function of CDK6 is crucial for its role in promoting myeloid and lymphoid malignancies, including acute myelogenous leukemia (AML) and acute lymphoblastic leukemia (ALL; refs. 1, 3), and is important in maintaining hematopoietic and leukemic stem cells (2, 7). Recently, CDK6 activity was shown to regulate metabolic functions in T-cell ALL (T-ALL), contributing to the transformed phenotype (8).
Thus, the potential of CDK4/6 inhibitors is widely acknowledged and CDK4/6 inhibitors are considered to represent a major breakthrough in cancer therapy (9). A number of clinical trials are starting and CDK4/6 inhibitors are being examined for possible use in patients with hematologic disorders.
One of the key factors in determining therapeutic outcome is the status of the p53 pathway (10). TP53 is among the most commonly mutated or deleted genes in human cancers, and aberrations of the p53 pathway are frequently associated with rapid disease progression and a poor prognosis (11). Nevertheless, the molecular networks that favor the development of p53 aberrations are not fully understood. When considering possible therapeutic options, it is vital to avoid doing anything that might cause the emergence of TP53 mutations. Precision medicine currently considers p53 status but could be improved by incorporating knowledge of factors that affect the mutational status of cancer cells. If a therapeutic approach is liable to provoke mutations, this point must be borne in mind when designing combinatorial or sequential approaches.
We now report that CDK6 counteracts p53-induced responses. Of note, high as well as low CDK6 expression levels have been shown to be of bad prognostic value, which is currently not understood. High levels of CDK6 are frequently found in malignant lymphoid diseases (12–15). In contrast, monoallelic loss of CDK6 (via 7q deletions or monosomy 7) in ALL, myelodysplastic syndrome (MDS), and AML is associated with a poor prognosis (16–20). Besides 7q deletions, CDK6 is a target of various miRNAs; a recent study described the downregulation of CDK6 by miR-145, which confers resistance to chemotherapy in lung cancer cell lines (21). Our study sheds light on these apparent contradictions; we show that CDK6 expression levels correlate with the status of the p53 pathway in murine and human tumors. CDK6 suppresses p53 responses upon oncogenic stress, inducing the transcription of a number of genes such as Prmt5, Ppm1d, and Mdm4, which negatively regulate p53 (22). Tumors with low or absent CDK6 expression are pressured to mutate TP53 to overcome oncogenic stress. Our findings imply that any therapy that interferes with CDK6 activity may be associated with a higher risk of acquiring TP53 mutations.
CDK6 Is Required to Support the Outgrowth of Malignant Cell Lines
Recent evidence highlights the role of CDK6 in malignant cells for tumor maintenance and progression and has revealed the importance of CDK4/6 inhibitors in the therapeutic landscape. In contrast, only limited information is available on its function during the transformation process and for tumorigenesis. We thus retrovirally infected primary bone marrow cells isolated from Cdk6+/+ and Cdk6−/− mice with a pMSCV-BCR–ABLp185-IRES-GFP vector and monitored leukemic cell outgrowth using the empty vector as control. The initial response of Cdk6+/+ and Cdk6−/− cells to transformation and exposure to the oncogene is comparable as the numbers of colonies forming in growth factor–free methylcellulose were superimposable 10 days after pMSCV-BCR–ABLp185-IRES-GFP infection (Supplementary Fig. S1A). Further, viable cells were present in equal numbers after 10 days in liquid culture, and no differences in apoptosis were detectable at that time point (Fig. 1A). Immune-phenotyping by FACS confirmed identical surface marker expression (CD19+CD43+B220+) in both cultures (Supplementary Fig. S1B). Irrespective of the genotype, we observed a pronounced initial increase in apoptotic cell numbers that rapidly ceased in Cdk6+/+ cells allowing the outgrowth of cell lines (Fig. 1A and B; Supplementary Fig. S1C). In Cdk6−/− cultures, the percentage of apoptotic cells remained high; GFP+ Cdk6−/− cells largely failed to escape the apoptotic response, which resulted in a delayed outgrowth and reduced numbers of BCR–ABL+ cell lines with a slightly increased BCR–ABL expression (Fig. 1A–C; Supplementary Fig. S1D). These observations indicated that CDK6 antagonizes apoptotic responses during transformation to allow for immortalization and outgrowth of primary transformed cells. Similar observations were made when we tried to establish cell lines from NPM–ALK transgenic mice on either a Cdk6+/+ or a Cdk6−/− background. Whereas cell lines were readily established from wild-type tumors, Cdk6−/− cell lines were established at a drastically lower frequency (data not shown).
Cdk6−/− Cells Acquire a Defective p53 Response during Transformation
Immortalization requires counterbalancing oncogenic stress via the p53–p19ARF pathway (23). To test whether Cdk6+/+ and Cdk6−/− cells harbor a normal p53 response, we first used γ-irradiation or the radiomimetic neocarzinostatin (NCS) in primary nontransformed lymphoid cells. We readily induced apoptosis in pre-pro-B cells ex vivo and in vitro as well as thymocytes ex vivo irrespective of the genotype (Supplementary Fig. S2A–S2D). In both cell types, the irradiation-induced apoptosis was preceded by increased expression of the p53 target genes Cdkn1a (encoding p21), Noxa, and Puma (Fig. 1D; Supplementary Fig. S2E and S2F). Differences became apparent in transformed cells: BCR–ABL+ Cdk6−/− cells displayed high p53 protein expression levels already in the absence of any stimulus (Fig. 1E). Accordingly, they failed to induce Cdkn1a, Noxa, and Puma upon NCS or etoposide treatment (Fig. 1F; Supplementary Fig. S2G and S2H) and to undergo apoptosis upon γ-irradiation, NCS, or etoposide treatment (Supplementary Figs. S2I, S2J and S3A–S3C), whereas BCR–ABL+ Cdk6+/+ cells readily underwent apoptosis upon any stimulus used. Similar results were obtained with nutlin-3 that directly stabilizes p53 by preventing its MDM2-dependent degradation (Supplementary Fig. S3D–S3G). Reexpression of CDK6 in immortalized cells did not reconstitute the ability to induce apoptosis (Supplementary Fig. S3H–S3J). To test the therapeutic significance of our observation, we transplanted BCR–ABL+ Cdk6+/+ and Cdk6−/− cells into NSG mice and subjected them to radiotherapy 5 days posttransplantation. In this model, the absence of CDK6 significantly delayed disease development. As expected, irradiation with 2.5 or 1.25 Gy significantly delayed disease progression in mice that had been transplanted with BCR–ABL+ Cdk6+/+ cells. In contrast, no changes in disease latency were detected in mice that had received BCR–ABL+ Cdk6−/− cells (Fig. 1G). Similarly, the analysis of chemosensitivity of BCR–ABL+ Cdk6+/+ and Cdk6−/− cells using a library of 272 FDA-approved drugs uncovered a drastically diminished response to standard chemotherapeutic drugs requiring an intact p53 response (Fig. 1H; Supplementary Fig. S4A). This included 5-FU, 6-mercaptopurine, 5-azacytidine, docetaxel, and doxorubicin; drug responses were recapitulated in a panel of individually derived BCR–ABL+ Cdk6+/+ and Cdk6−/− cell lines (Supplementary Fig. S4B). Importantly, differences in drug responses were uncoupled from changes in cell-cycle distribution (Supplementary Fig. S4C–S4H). Analysis of the Trp53 mutational status uncovered Trp53 mutations in all Cdk6−/− cell lines including cell lines harboring a point mutation in the kinase domain of CDK6, rendering them kinase inactive (Cdk6K43M; ref. 24). In contrast, only one of eight Cdk6+/+ lines investigated had acquired a Trp53 mutation (Fig. 2A; Supplementary Fig. S5A; Cdk6+/+ vs. Cdk6K43M P = 0.01; Cdk6+/+ vs. Cdk6−/− P = 0.005). All Trp53 mutations localized within the DNA-binding domain and have been described to disrupt p53 responses (25–27). Apoptosis could be induced following addition of PRIMA-met, which reactivates p53, showing that the inability of Cdk6−/− cell lines to undergo apoptosis stems solely from the Trp53 mutations (Fig. 2B; Supplementary Fig. S5B). In summary, these findings led us to conclude that cells are forced to mutate p53 to compensate for the absence of CDK6 during transformation. Accordingly, transformants derived from Trp53−/−;Cdk6−/− double deficient animals were able to escape oncogene-induced apoptosis in contrast to GFP+ Trp53+/+;Cdk6−/− cells (Fig. 2C and D). These data indicate that the major function of CDK6 during transformation and immortalization is to counteract oncogene-induced p53-mediated apoptotic responses.
CDK6 Induces a Transcriptional Program to Antagonize p53 during Transformation
To investigate how CDK6 counteracts p53, we decided on an unbiased approach. We transduced Cdk6+/+, kinase-dead Cdk6K43M mutant, and Cdk6−/− primary bone marrow with BCR–ABL followed by cloning of the cells in growth factor–free methylcellulose. Individual colonies were picked at day 10 as this time point represents the critical phase where CDK6 is required to counteract p53. Gene expression analysis by microarrays of single Cdk6+/+, Cdk6K43M, and Cdk6−/− colonies (n = 4 of each genotype) revealed a highly consistent pattern and identified 4,471 genes that are regulated in a kinase-dependent manner that showed an altered regulation in Cdk6−/− and Cdk6K43M colonies (Fig. 3A; Supplementary Fig. S6A and S6B). Gene ontology analysis of enriched biological processes identified the execution phase of apoptosis, DNA damage response, and signal transduction by p53 class mediator as the main deregulated processes (Fig. 3B; Supplementary Table S1). Several of the deregulated genes have been reported to promote tumorigenicity (exemplified in Fig. 3B) including the BCL2-regulator Rrm2 and prominent p53 antagonists such as Mdm4, Ppm1d, and the methyltransferase Prmt5. Ppm1d, Prmt5, and Mdm4 are part of a tightly controlled negative feedback regulator network that ensures limitation of the p53 response after cell-cycle arrest and subsequent DNA damage repair (28). In total, we were able to identify 22 of 59 well-established p53 regulators by comparing our list of deregulated genes with the PID p53 regulation pathway gene set (29). Validation by qPCR in individually derived single colonies and western blot analysis in outgrowing BCR–ABL+ cells verified the fidelity of our microarrays (Fig. 3C; Supplementary Fig. S6C and S6D). Besides regulating p53 itself, PRMT5 has been shown to interfere with MDM4 mRNA splicing (30), which was also evident in colonies lacking kinase-active CDK6 that displayed increased levels of aberrantly spliced MDM4 mRNA (MDM4-S; Supplementary Fig. S6E). At this time point, the cells were sensitive to CDK6 reexpression: when we performed a rescue experiment and cotransduced CDK6 and BCR–ABL in Cdk6−/− bone marrow cells, we found a positive correlation between Cdk6 and Mdm4 and between Cdk6 and Prmt5 mRNA in single colonies (Supplementary Fig. S6F). Cells that do express CDK6 were capable of inducing the p53 antagonists MDM4 and PRMT5.
Different Transcriptional Responses Are Induced by Chromatin-Associated CDK6 during and after Transformation
To elucidate how CDK6 regulates transcription, we decided again on an unbiased approach and performed RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analysis in BCR–ABL+ Cdk6+/+, Cdk6K43M, and Cdk6−/− cell lines. RNA-seq analysis identified 1,785 genes that are regulated in a CDK6-dependent manner (Fig. 3D). Analysis of the gene ontology terms among the deregulated genes identified biological processes including regulation of the immune response, cytoskeleton, and wound healing as major enriched (Supplementary Table S2) in established cell lines. In the initially transformed cells, responses to stress were the dominating biological process (Supplementary Fig. S7A). ChIP-Seq analysis was used to define directly transcriptionally controlled genes using antibodies against CDK6. We identified CDK6 bound to the promoter regions of 2,468 genes deregulated in CDK6-deficient versus Cdk6+/+ BCR–ABL+ colonies and to the promoter regions of 706 genes in cell lines (Fig. 3E). Importantly, 16 of 22 differentially expressed p53 regulators including Mdm4, Prmt5, and Ppm1d display a peak in their respective promoter region (exemplified in Fig. 3F; Supplementary Fig. S7B). The binding of CDK6 to the Mdm4 and Prmt5 promoters was validated by ChIP followed by qPCR analysis (Supplementary Fig. S7C). ChIP-seq results were verified in cells expressing an HA-tagged version of CDK6 (Supplementary Fig. S7D). Gene ontology analysis focusing on deregulated genes with a CDK6 peak identified responses to stress signals such as execution phase of apoptosis and signal transduction in response to DNA damage as a major deregulated pathway in the initially transformed cells highlighting the role of CDK6 in this process (Fig. 3G; Supplementary Fig. S7E; Supplementary Table S3). In contrast, regulation of cellular component size, embryonic morphogenesis (developmental processes), regulation of growth, locomotion, and intracellular signal transduction were predominant in established cell lines (Supplementary Table S4). As expected, regulation of cell cycle was identified as a common CDK6-dependent pathway during initial transformation and in established cells. Furthermore, we found intracellular signal transduction pathways including IκB kinase/NF-κB, MAPK, and JAK–STAT signaling as commonly regulated (Supplementary Fig. S7E; Supplementary Table S5).
NFY and SP1 as Mediators of CDK6-Dependent Transcription
CDK6 as a cofactor does not contain a DNA-binding domain; thus, we performed a motif enrichment analysis to understand how CDK6 interacts with chromatin. NFY and SP1 were identified as common denominators in cell lines and colonies underlying ChIP-seq peaks associated with altered gene transcription (Fig. 4A). Further evidence for a role of NFY and SP1 was obtained in an assay where we analyzed differentially phosphorylated chromatin-bound proteins (phospho-chromatome) in bone marrow cells 10 days after BCR–ABL transduction either expressing or lacking CDK6 (Fig. 4B). This assay analyzes the phosphorylation status by quantitative mass spectrometry and uncovered NFYA and SP1 among the top differentially phosphorylated proteins in the critical phase when differences between Cdk6+/+ and Cdk6−/− cells became evident (Fig. 4C). Significant differences were also uncovered for the transcription factors ZBTB7A, EBF1, YY1, ELF2, and ATF1 (Fig. 4C). Changes in the nonphosphorylated chromatome fraction confirmed reduced levels of PRMT5, MDM4, and PPM1D upon CDK6 deficiency (Supplementary Fig. S8A). To test whether the identified transcription factors are subject to direct phosphorylation by CDK6, we performed in vitro kinase assays. Indeed, active CDK6/Cyclin D3 complexes phosphorylate recombinant NFYA, ZBTB7A, and EBF1 in a dose-dependent manner, a response that was suppressed by addition of the CDK4/6 kinase inhibitor palbociclib (Fig. 4D and E; Supplementary Fig. S8B). Coimmunoprecipitation, ChIP, and ChIP-ReChIP experiments in BCR–ABL+ cell lines verified the interaction between NFYA and CDK6 (Fig. 4F and G; Supplementary Fig. S8C and S8D). Similar to CDK6 knockdown experiments, knockdown of NFYA and SP1 in initially transformed cells induced apoptosis that was partially counteracted in the absence of p53 (Supplementary Fig. S8E).
As SP1 and NFY were reported to cooperate with p53 at promoter regions, we next overlaid the CDK6 ChIP-seq peaks with p53 ChIP-seq data performed in untreated or γ-irradiated B cells (31). In untreated cells, we identified a low number of promoter regions bound to p53 and CDK6 (4%; Fig. 5A). In γ-irradiated cells, this situation changed dramatically with more than 50% of all CDK6 peaks showing an overlay with p53 peaks (9,304 common peaks). The perfect alignment of the peak positions suggested common binding sites for CDK6 and p53 (Fig. 5A), which again were enriched for NFY and SP1 motifs (Supplementary Fig. S8F). As we failed to obtain any evidence for a direct interaction between CDK6 and p53 (Supplementary Fig. S9A and S9B), we speculated that p53 competes with CDK6 for binding to the promoters of p53 antagonists Prmt5, Ppm1d, and Mdm4 via NFY or SP1. In line with the p53 ChIP-seq data, we observed a nutlin-3–induced increase in p53 bound to the promoters of Prmt5, Mdm4, and Ppm1d, which was paralleled by a decrease of promoter-bound CDK6 (Fig. 5B). Global analysis of binding sites for p53 revealed that hardly any direct p53-binding sites were detected in the overlap of p53/CDK6 (Fig. 5C and D). CDK6 and p53 seem to predominantly interact via SP1 and NFY to regulate transcription, as evident when analyzing the p53-negative regulators Prmt5, Ppm1d, and Mdm4 (Fig. 5E). In contrast, the direct p53 targets and apoptosis inducers Puma and Noxa were not altered by the absence of CDK6 (Supplementary Fig. S9C). Based on these data, we propose a model where CDK6 directs p53-dependent transcription by phosphorylating NFY and SP1 (Fig. 5F).
The CDK6-Associated Gene Signature Is Conserved in Human and Murine Leukemia
To study whether our findings in murine models of leukemia relate to human patients, we analyzed publicly available gene expression signatures from patients with BCR–ABL+ and BCR–ABL− ALL and MDS (international multicenter studies TARGET and MILE). We initiated an unbiased approach: Pairwise Spearman correlation coefficients of mean CDK6 gene expression to all other probes on the array were calculated, and biologically informative genes were selected by implementing a permutation-based test. Gene set enrichment analysis was used to identify common pathways under control of CDK6: E2F-dependent transcriptional programs were used as positive control and showed a clear correlation in a human gene list ranked according to the correlation coefficient (Supplementary Fig. S10A; Supplementary Table S6). When we analyzed the enrichment of p53 regulators and NFY target genes, we found a significant positive correlation across all disease entities verifying the global nature of our finding (Fig. 6A; Supplementary Fig. S10B; Supplementary Table S6). Overlapping the CDK6-dependent human gene patterns identified 377 genes that were consistently correlated to CDK6 levels (Fig. 6B; Supplementary Fig. S11A–S11D). Among the genes that were consistently deregulated in a CDK6-dependent manner were the p53 negative regulator PRMT5 and the antiapoptotic gene BCL2 (Fig. 6C; Supplementary Fig. S6C).
Our data would also predict that low CDK6 levels predispose human tumors to p53 mutations or alternatively that high CDK6 levels may substitute for p53 mutations. We analyzed two datasets that allowed correlating the p53 status to CDK6 levels, a patient cohort suffering from thymoma and one from glioblastoma. In both diseases, CDK6 is frequently expressed at high levels (12, 32). In both independent datasets, a higher incidence of p53 mutations accompanied low CDK6 levels. (Supplementary Fig. S11E). For glioblastoma, the analysis reached statistical significance, and the cohort of patients with thymoma was smaller but showed a clear trend (Supplementary Fig. S11E).
Another means of testing this concept is to study patients with a 7q deletion that includes the CDK6 gene. We thus investigated two cohorts of patients with hematologic malignancies. The first cohort consisted of 187 patients with AML and 202 patients with ALL, and the second cohort included 697 cases of MDS. In the first cohort, 17 patients had a genomic loss of the CDK6 locus on chromosome 7, which is proposed to include CDK6, and 48 patients had mutations in TP53. Monoallelic loss of CDK6 resulted in a decreased CDK6 expression in AML and ALL (Supplementary Fig. S11F). Within the patients with AML/ALL (n = 389), 7 patients (1.8%) were found to harbor mutations in TP53 and loss of CDK6 (Fig. 6D). Within the 697 patients with MDS, 90 patients (12.9%) had mutations in TP53 and 93 (13.3%) had losses on chromosome 7 (Fig. 6D). Thirty-eight cases (5.5%) had both mutations in TP53 and losses on chromosome 7q (Fig. 6D). In summary, we have observed patients with both anomalies far more frequently than would be expected by chance (P < 0.0001).
Recent discoveries have shed new light on CDK6. The protein was initially described as a cell-cycle kinase with a function highly redundant to that of its close homolog CDK4. CDK6 is now also known to be a transcriptional regulator whose targets include important proto-oncogenes such as VEGFA and FLT3 (1, 3). In this study, we define CDK6 as a prosurvival factor that is required to counteract p53 responses, which are of utmost importance during the early phases of transformation and immortalization. The CDK6-mediated antagonism of stress responses is mirrored in human hematopoietic malignancies such as ALL, AML, and MDS, and its significance is not restricted to BCR–ABL-induced transformation, which we used as a model. CDK6′s wide-ranging effects on transcription are highlighted by the numerous specific ChIP-seq peaks associated with transcriptional changes. The fact that CDK6 regulates slightly distinct transcriptional programs in transformed and established cell lines confirms that this versatile protein has different functions during the multistage process of tumor formation and leukemogenesis.
Although CDK6 is of minor importance under homeostatic conditions, it assumes a prominent role under conditions of stress, such as hematopoiesis (2, 7) and oncogene-induced stress. The importance of CDK6 in stress responses is clear in our global analysis; gene expression data and the CDK6 ChIP-seq analysis reveal “cellular stress responses” as major regulated processes during the transformation phase. A p53 response represents the cell's reaction to stress, activating pathways that lead to apoptosis or to DNA repair and survival (33). CDK6 may assume particular importance in situations with high levels of MYC and thus a pronounced proapoptotic response. It has been shown that activation of the stress kinase p38 translocates CDK6 into the nucleus, where it is available to induce transcriptional responses (4).
Oncogene-induced stress (OIS) represents a particular case. CDK6 is at the intersection of the two major pathways of tumor formation: It regulates RB via p16INK4A to arrest the cell cycle, enabling DNA repair, while counteracting p53-induced apoptosis and thereby promoting cell survival. Of note, Cyclin D1 has also been assigned a role in DNA damage repair, which represents a hallmark of OIS. Cyclin D1 is recruited to sites of DNA damage; the role for CDK4/6 in this context remained elusive (34). We observed a divergent regulation of p16INK4A and p19ARF in Cdk6+/+ and Cdk6−/− cells. Whereas Cdk6+/+ cells initially upregulate and then silence p16INK4A expression upon BCR–ABL transformation (1), Cdk6−/− cells induce p19ARF, which remains consistently high in CDK6-deficient p53-mutant cells (Supplementary Fig. S12). The cause and consequences of these findings remain to be determined.
Cells with little or no CDK6 frequently show mutations in p53 to maintain a transformed state in mice and men. Transformed Cdk6−/− cells are insensitive to irradiation and respond poorly to classic chemotherapeutic drugs. Deletion of p53 in the absence of CDK6 enables cells to establish cell lines at normal rates, confirming that CDK6 functions primarily to antagonize p53. The underlying transcriptional program includes many genes whose promoter regions are directly bound by CDK6, including Ppmd1, Prmt5, and Mdm4. Although the initial discovery of a CDK6 function in regulating gene transcription suggested it to be independent of the kinase activity, CDK6 controls the transcription of p53 regulators in a kinase-dependent manner linking cell-cycle control to the regulation of apoptosis. Several lines of evidence point at a central role for the transcriptional regulators NFY and SP1 as integrative elements at the crossroads of p53 and CDK6. Although SP1 and NFY have previously been described to be involved in p53-mediated transcription (35–37), our study puts them in a novel context: We propose that CDK6 phosphorylates NFY and SP1 to allow p53 to act as a transcriptional regulator. The absence of CDK6 thereby shifts the delicate balance upon p53 activation toward apoptosis.
The fact that CDK6 protects transformed cells from oncogenic stress and from DNA damage–induced apoptosis may account for the apparent selection for cells with high levels of CDK6 in vivo. CDK6 suppresses p53-mediated stress responses while supporting oncogene-induced replication. Although cells are usually more vulnerable to cell death when proliferating, CDK6 not only promotes cell division but also helps protect proliferating cells from p53-induced cell death. Inhibition of CDK4/6 kinase activity leads to growth arrest and impairs responses to classic chemotherapeutics, but our data predict that a specific CDK6 inhibitor, which would not target CDK4 and would thus have little effect on proliferation, should enhance p53-induced drug responses.
p53 is itself subject to complex regulation (38), and we show that CDK6 is part of the network that regulates p53 responses during transformation. Different cell types may react differently to CDK6 inhibition: Senescence dominates in epithelial cells, whereas treatment of hematologic malignancies with CDK4/6 inhibitors can induce apoptosis (39, 40). The molecular explanation has remained obscure but the finding that CDK6 regulates the p53 response provides a tantalizing hint: p53 links senescence and apoptosis, balancing the two pathways and directing a cell type in one or the other direction. We propose that CDK6 regulates p53. The CDK6-mediated transcriptional responses are highly conserved in mouse and man and are relevant in human tumors. We found a correlation between levels of CDK6 and occurrence of p53 mutations. The finding that CDK6 is controlled by a superenhancer in glioblastoma, multiple myeloma, T-ALL, and colorectal cancer suggests that the level of CDK6 may be a predictive factor in several disease entities (41). Our data also suggest that the association of 7q deletions and monosomy 7 with p53 mutations (19, 42–45) is related to CDK6 (46, 47): 7q deletions and monosomy 7 (which remove CDK6) enhance the risk of a concomitant p53 mutation by a factor of four.
These findings have consequences for our assessment on the effects of CDK4/6 inhibitors. They may be helpful to eliminate leukemic stem cells; a recent study showed that BCR–ABL+ CML stem cells are sensitive to a combination of MYC inhibition and p53 induction. CDK6 inhibition in the light of p53 activation may be exploited in combination therapies to sensitize leukemic stem cells that are resistant to tyrosine kinase inhibitor therapy (48). On the other hand, persistent inhibition of CDK6 may provoke the outgrowth of p53-mutant clones, so patients who receive CDK6 inhibitors may be at risk of developing p53 mutations. CDK6 inhibition may allow premalignant cells and cancer-initiating cells to progress to a more malignant stage, for example in patients with clonal hematopoiesis, who are at a higher risk of developing hematopoietic malignancies such as MDS or AML (49). CDK6 inhibitors should therefore be used at precise doses and times. Although this poses a challenge for precision medicine, the availability of CDK6 inhibitors represents a unique opportunity to target the two key pathways in tumor formation, as CDK6 not only regulates RB by direct phosphorylation but also interferes with p53-dependent responses.
Description of MDM4 mRNA splicing analysis, phospho-chromatome, kinase assay, microarray, motif enrichment analysis, and analysis of publicly available leukemia datasets is contained in the supplementary data.
Preparation of retroviral supernatant and generation of BCR–ABL+ cell lines was performed as described previously (1). Briefly, single-cell suspensions from bone marrow were transduced with a pMSCV-BCR–ABLp185-IRES-GFP vector, and the outgrowth of cell lines was monitored by microscopic inspection and FACS analysis. Cell lines were maintained in RPMI medium supplemented with 10% FCS, 50 μmol/L 2-mercaptoethanol, 100 U/mL penicillin, and 100 μg/mL streptomycin (PAA). To preserve integrity, BCR–ABL+ cell lines were expanded and frozen down into a large number of cryogenic vials upon generation. Experiments were performed with cells passaged less than 6 months after generation. For the reexpression of CDK6 and HA-CDK6 in Cdk6−/− cell lines, pMSCV-IRES-puromycin and pMSCV-IRES-GFP plasmids were used. Pre-pro-B cells were derived and cultured as described previously (50). Knockdown experiments were performed using the pLENC plasmid (pMSCV-miRE-PGK-NeoR-IRES-mCherry; ref. 51). Transient transfection experiments were performed using the Neon Transfection System (Invitrogen) according to the manufacturer's instructions. shRNA sequences are included in the supplementary data.
All mice were maintained on a C57BL/6J background (Cdk6−/−, ref. 52; Trp53−/−, ref. 53) under specific pathogen-free conditions at the University of Veterinary Medicine Vienna (Vienna, Austria). NOD/SCID/IL2Rγ−/− (NSG) mice were purchased from The Jackson Laboratory. In all experiments, 6- to 8-week-old mice were used. Animal experiments were performed in accordance with protocols approved by the Austrian law and the Animal Welfare Committee at the University of Veterinary Medicine Vienna (License BMWFW-68.205/0045-WF/V/3b/2016).
Drug screening was performed as described previously (3). Briefly, 50 nL of each compound was spotted into 384-well plates (Corning 3701) and cells were incubated in the presence of drugs for 72 hours. Thirty-two negative control wells (DMSO) and 32 positive control wells (Bortezomib, concentration 10 μmol/L) were prepared on each plate. Data from these wells were used to calculate a Z′-factor for each plate individually (54). Cell viability was assessed using CellTiter-Glo (Promega). To compare compounds across plates with different signals, a percentage of control was calculated by linear regression. Thereby, DMSO was set to 100% and bortezomib to 0% viability. Hits were defined as compounds when inhibition was above 50% compared to the DMSO controls.
Western Blotting and Immunoprecipitation
Cell lysis, immunoprecipitation, blotting, and blocking was performed as described previously (55). For western blot analysis of initially transformed cells, density gradient centrifugation (Histopaque-1077, Sigma-Aldrich) was performed to isolate viable lymphocytes. NFYA was immunoprecipitated using the NFYA (G-2, Santa Cruz Biotechnology) antibody. For immunoprecipitation of HA-tagged CDK6, Pierce Anti-HA Magnetic Beads (Thermo Fisher Scientific) were used. The following antibodies were used for detection: PRMT5 (07-405, 1:1,000; Merck-Millipore), GAPDH (ABS16, 1:1,000; Merck-Millipore), pY207Crkl (1:500; Cell Signaling Technology), pS780Rb (1:500; Cell Signaling Technology), HA (ab9110, 1:1,000; Abcam), CDK4 (H-22; 1:1,000), CDK6 (H96 and DCS90; 1:1,000), NFYA (G-2; 1:1,000), β-actin (AC-15; 1:1,000), BCL2 (N-19; 1:1,000), p53 (DO-1; 1:1,000), p21 (F-5; 1:1,000), and HSC70 (B-6; 1:1,000), all from Santa Cruz Biotechnology.
Cells were stained using antibodies directed against CD43-APC, B220-FITC, CD19-APC-Cy7, CD43-PE, streptavidin-APC-Cy7, IgM-FITC, CD16/CD32 Fc receptor block (all from BD Biosciences; 1:100); CD19-eFluor450, B220-PerCP-Cy5.5, BP1-biotinylated, and IgD-APC (all from eBioscience). For cell-cycle analysis, 1 × 106 cells were stained with propidium iodide (PI; 50 μg/mL) in a hypotonic lysis buffer (0.1% sodium citrate, 0.1% Triton X-100, 100 μg/mL RNAse) and incubated at 37°C for 30 minutes. Analysis of apoptotic fractions was performed by staining with Annexin V and 7-AAD from the Annexin V Apoptosis Detection Kit eFluor 450 (eBioscience) following the manufacturer's instructions. Samples were analyzed using a FACS Canto II flow cytometer equipped with 488, 633, and 405 nm lasers (BD Biosciences). FACS-Plots and calculations were done using the FACS Diva software version 6.1.2 (BD Biosciences). High-purity FACS sorting was performed on a FACSAria III equipped with a 488 nm laser (BD Biosciences) as described previously (2).
Colony Formation Assay
Bone marrow cells from 6-week-old donor mice were transduced with retroviral supernatants as described previously (1). Twenty-four hours thereafter, the cells were harvested and embedded into growth factor–free methylcellulose (MethoCult) at a density of 5 × 105 cells/mL. After 10 days, colonies were counted using an inverted microscope device (CKX41, Olympus) and picked for RNA extraction.
RNA Isolation, PCR, and qPCR Analysis
Total RNA was isolated from individual colonies as well as from stable cell lines. RNA was extracted using the RNeasy Micro Kit (Qiagen). Reverse transcription was performed using the iSCRIPT cDNA Synthesis Kit following the manufacturer's instructions (Bio-Rad). All qPCRs were performed in duplicate with the SsoFast EvaGreen Supermix (Bio-Rad) according to the instructions of the manufacturer. Primer information and detailed thermocycling conditions are available upon request. Levels of mRNAs were normalized to Rplp0 mRNA.
For analysis of the Trp53 mutational status, the p53 coding sequence was amplified using the following primer pairs:
PCR products were purified from a 3% agarose gel using the E.Z.N.A. Gel Extraction Kit (OMEGA) and Sanger-sequenced by Microsynth AG.
RNA was isolated from immortalized BCR–ABL+Cdk6+/+ and Cdk6−/− cells. Single-end, 50 bp sequencing of libraries prepared with the Lexogen SENSE mRNA-Seq Library Preparation Kit was performed on an Illumina HiSeq-2500 sequencer. After quality control of raw data with FASTQC and removal of adapters and low-quality reads with trimmomatic (version 0.36), reads were mapped to the Genecode M13 genome using STAR (version 2.5.2b) with default parameters. Then, the featureCounts tool of the Subread package (version 1.5.1) was used to obtain gene counts for union gene models. Differentially expressed (Padj < 0.05) genes were identified using DESeq2 (version 1.16.1). For heat maps, centered and scaled rlog transformed library size normalized counts were visualized using the heatmap.2 function of the R package gplots version 3.0.1. The RNA-seq data reported in this article have been deposited in the Gene Expression Omnibus (GEO) database (Accession ID: GSE113752).
ChIP and ChIP-ReChIP experiments were performed using homemade anti-CDK6 rabbit polyclonal serum (BioGenes), or antibodies against HA (ab9110, Abcam), NFYA (G-2, Santa Cruz Biotechnology), and p53 (CM5, Novocastra; refs. 1–3). For ChIP-seq analysis, sequencing reads were quality controlled by FASTQC. Quality filtering, trimming of reads, and adapter removal were done by using trimmomatic (version 0.36). Quality filtered reads were mapped against the mouse reference genome (Gencode M13) with bwa-mem (version 0.7.15). Blacklisted regions were removed from the analysis using bedtools subtract (version 2.26.0). Multimapping reads and reads with bad mapping quality were removed by removing reads with a mapping quality below 10 using samtools (version 1.3.1). Peak calling was performed by MACS2 (version 2.1.0) using default parameters. For validation, coimmunoprecipitated DNA was analyzed by qPCR (EpiTect HRM PCR Kit, Qiagen) for Prmt5, Mdm4, Ppm1d, Cdk1, Sp1, and Cyclin G1 promoter sites using a MyiQ device (Bio-Rad). Binding to the murine Vegfa promoter was used as positive control for CDK6 ChIP and the p21 promoter for p53 ChIP (1–3). Antibody specificity was evaluated by including an IgG isotype control (02-6102, Invitrogen). Binding to a negative control region downstream of murine CD19 was used to estimate nonspecific binding (1–3). Primer sequences are listed in the Supplementary Section. The ChIP-seq data reported in this article have been deposited in the Gene Expression Omnibus (GEO) database (Accession ID: GSE113752).
Motif Enrichment Analysis
The peaks of the ChIP-seq analysis were annotated with Homers annotate.pl script and default parameters. Only peaks within promoter regions (promoter-TSS tag) were considered for further analysis. The intersect between closest downstream genes of these promoter peaks and differentially regulated genes found within colonies, RNA-seq, and both was computed. The promoter peaks of these intersected genes were used for a motif enrichment analysis using Homer (v.4.9.0) findMotifsGenome.pl with the default -size 200. The motif analysis was performed 3 times and motifs that (i) were not marked as potentially false positives within Homers de novo results and (ii) appeared in at least 2 replicates were counted as present. Density plots were generated by Homers annotate.pl script with a range of −500 to +500 bp from peak summit for SP1, NFY, and p53 motifs obtained from the motif enrichment analysis.
Statistical analyses were performed using ANOVA, Fisher exact test for the p53 mutations in the mouse cell lines, or the Student t or log-rank tests (GraphPad Prism 5). All data are shown as mean ± SD or ± SEM. Probabilities of P < 0.05 were considered significant. For the analysis of an association between -7/del(7q) and the p53 mutational status in ALL, AML, and MDS samples, the χ2 test was used.
Disclosure of Potential Conflicts of Interest
M. Malumbres reports receiving commercial research grants from Pfizer and Lilly. No potential conflicts of interest were disclosed by the other authors.
Conception and design: F. Bellutti, J. Zuber, A. Villunger, K. Kollmann, V. Sexl
Development of methodology: J.I. Loizou, M. Farlik
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): F. Bellutti, A.-S. Tigan, S. Nebenfuehr, R. Grausenburger, S. Hartenberger, S. Kollmann, E. Doma, M. Prchal-Murphy, I.Z. Uras, A. Höllein, B.L. Ebert, A. Ringler, A.C. Mueller, P.W. Hinds, S. Kubicek, M. Malumbres, K. Kollmann
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): F. Bellutti, M. Dolezal, M. Zojer, R. Grausenburger, A. Höllein, D.S. Neuberg, A. Ringler, A.C. Mueller, C. Vogl, G. Heller, S. Kubicek, J. Zuber, M. Malumbres, M. Farlik, K. Kollmann, V. Sexl
Writing, review, and/or revision of the manuscript: F. Bellutti, D.S. Neuberg, B.L. Ebert, J.I. Loizou, M. Farlik, V. Sexl
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.C. Mueller, G. Heller
Study supervision: V. Sexl
We are deeply indebted to G. Tebb for critical discussions and editing of the manuscript. We are also grateful to D. Partida, A. Gundacker, P. Kudweis, R. Idris, S. Fajmann, and P. Jodl who provided valuable technical support. We are indebted to M. Kleiter and S. Kosik for help with the γ-irradiation experiments and to our animal caretaker team for great support. We want to thank G. Superti-Furga, T. Decker, R. Kralovics, M. Sibilia, Mathias Müller, and R. Moriggl for scientific input and discussions. The director of the Medical University of Vienna, Markus Müller, and the European Office enabled access to the data from the TARGET study. We thank the MLL Munich Leukemia Laboratory for great support and help. The work was supported by FWF-SFB47, FWF-P24297, and an ERC advanced grant to V. Sexl.
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