The majority of proposed anticancer treatments do not succeed in advancing to clinical use because of problems with efficacy or toxicity, often for unclear reasons. Lin et al. discovered that a drug candidate in clinical development was effective at killing cancer cells even when its target protein was knocked out, suggesting that its proposed mechanism of action was incorrect. The researchers then identified multiple drugs with similar problems and also discovered the correct target for one of them, suggesting that more research and more stringent methods are needed to verify the targets of potential drugs before advancing them to the clinic.

Ninety-seven percent of drug-indication pairs that are tested in clinical trials in oncology never advance to receive U.S. Food and Drug Administration approval. While lack of efficacy and dose-limiting toxicities are the most common causes of trial failure, the reason(s) why so many new drugs encounter these problems is not well understood. Using CRISPR-Cas9 mutagenesis, we investigated a set of cancer drugs and drug targets in various stages of clinical testing. We show that—contrary to previous reports obtained predominantly with RNA interference and small-molecule inhibitors—the proteins ostensibly targeted by these drugs are nonessential for cancer cell proliferation. Moreover, the efficacy of each drug that we tested was unaffected by the loss of its putative target, indicating that these compounds kill cells via off-target effects. By applying a genetic target-deconvolution strategy, we found that the mischaracterized anticancer agent OTS964 is actually a potent inhibitor of the cyclin-dependent kinase CDK11 and that multiple cancer types are addicted to CDK11 expression. We suggest that stringent genetic validation of the mechanism of action of cancer drugs in the preclinical setting may decrease the number of therapies tested in human patients that fail to provide any clinical benefit.

While screening cancer drug targets, we found that maternal embryonic leucine zipper kinase (MELK), a protein previously reported to be essential in multiple cancer types, could be eliminated using CRISPR-mediated gene editing without any detectable loss in cancer cell fitness ( 3 , 4 ). In addition, we demonstrated that OTS167, a small-molecule inhibitor of MELK undergoing phase 2 clinical trials, continued to kill MELK–knockout (KO) cancer cells with no decrease in potency. These findings suggested that a drug tested in human cancer patients had been designed to target a nonessential cellular protein and that its putative inhibitor killed cells by interacting with proteins other than its reported target. We hypothesized that problems in drug development and inhibitor validation, as exemplified by MELK and OTS167, could potentially contribute to the high failure rate of new cancer therapies. In particular, drugs that target superfluous proteins may display limited efficacy in human patients, and if these drugs are active only via off-target effects, then this could potentially contribute to patient toxicity. Moreover, clinical trials that use a biomarker to select patients for trial inclusion are about twice as likely to succeed as those without one ( 5 ). Misidentifying a drug’s mechanism of action (MOA) could hamper efforts to uncover a biomarker capable of predicting therapeutic responses, further decreasing the success rate of clinical trials. To test whether other cancer drugs had similarly been designed against nonessential targets or had been assigned an incorrect MOA, we set out to systematically analyze multiple cancer drugs and drug targets that have entered clinical trials or are in late-stage preclinical development.

Substantial progress has been made in the treatment of certain malignancies by targeting cancer “addictions” or genetic dependencies that encode proteins required for the survival and/or proliferation of cancer cells ( 1 ). Therapeutic agents that block the function of a cancer dependency—such as the kinase inhibitor lapatinib in HER2 + breast cancer—can trigger apoptosis and durable tumor regression ( 2 ). Discovering and characterizing druggable cancer dependencies are key goals of preclinical research.

RESULTS

CRISPR competition assays to investigate several putative cancer dependencies Based on an analysis of the literature, we chose drug targets that met several criteria (described in detail in Materials and Methods). Notably, we selected drug targets that had been reported to play a cell-autonomous role in cancer growth, such that their loss or inhibition was reportedly sufficient to block cancer cell proliferation. In addition, we selected drug targets that lacked a known mutation capable of conferring resistance to their targeted inhibitors, which we hypothesized represents the gold standard for proving a drug’s MOA. We identified 10 cancer drugs targeting six proteins that met these criteria (Table 1). Five of these proteins are reported to represent cancer dependencies (HDAC6, MAPK14/p38α, PAK4, PBK, and PIM1) (6–15). One protein (CASP3/caspase-3) is reported to induce apoptosis when activated by a small molecule (16, 17) and is discussed separately. Among the putative dependencies, over 180 different publications indicate that they are required for cancer cell proliferation or fitness (listed in data file S1). For each of these genes, the majority of evidence supporting their designation as cancer dependencies comes from RNA interference (RNAi) studies, in which small interfering RNA (siRNA)– or short hairpin RNA (shRNA)–mediated knockdown was reported to impair cancer cell fitness. In addition, each protein is targeted by one or more small-molecule drugs, which have been described to exhibit potent cell killing in vitro and in vivo. On the basis of these preclinical results, the drugs listed in Table 1 have been used in at least 29 different clinical trials, with an estimated enrollment of more than 1000 patients. Table 1 Anticancer drugs and drug targets. View this table: We first set out to validate the role of the putative dependencies targeted by these drugs in cancer cell fitness. To accomplish this, we applied a CRISPR-Cas9–based cell competition assay, in which cancer cells are infected at a low multiplicity of infection with green fluorescent protein (GFP)–expressing guide RNA (gRNA) vectors targeting a gene of interest (Fig. 1A) (18). If a CRISPR-induced mutation reduces cell fitness, then the untransduced cells within a population should outcompete the gRNA-expressing cells, and the fraction of GFP+ cells should decrease over time. To verify this approach, we designed gRNAs against pan-essential genes and against several confirmed cancer drug targets. In breast cancer, colorectal cancer, lung cancer, and melanoma cell lines, guides targeting the essential replication proteins RPA3 and proliferating cell nuclear antigen (PCNA) dropped out up to 100-fold, and guides targeting the validated pan-cancer dependencies Aurora A, Aurora B, and ERCC3 exhibited similar levels of depletion (Fig. 1B). Mutations in Aurora A (19), Aurora B (20), and ERCC3 (21) confer resistance to the cytotoxic agents MLN8054, ZM447439, and triptolide, respectively, thereby providing genetic evidence that they are required for cancer cell growth. In contrast, gRNAs targeting the nonessential Rosa26 and AAVS1 loci exhibited minimal dropout over five passages in culture. These GFP competition assays were also capable of identifying cell type–specific dependencies: Guides targeting the oncogenic kinase BRAF dropped out in a BRAF-mutant melanoma cell line but not a BRAF–wild-type (WT) colorectal cancer line, whereas guides targeting the gene encoding the estrogen receptor (ESR1) dropped out in an ER-positive breast cancer line but not in a triple-negative breast cancer line (Fig. 1C). We concluded that our CRISPR dropout assay can robustly identify both pan-essential and cancer type-specific genetic dependencies. Fig. 1 Cell competition assays to test the essentiality of putative cancer dependencies. (A) Schematic of the CRISPR-based cell competition assays used in this paper (18). (B) Cell competition assays comparing guides targeting AAVS1 and ROSA26 (nonessential, negative control genes), RPA3 and PCNA (pan-essential positive control proteins), and Aurora A, Aurora B, and ERCC3 (inhibitor-validated cancer dependencies). Full results from these competition experiments are included in data file S2. (C) Cell competition assays for the cell type–specific cancer dependencies BRAF and ESR1. (D) Western blot analysis of A375 populations transduced with the indicated gRNAs. (E) Cell competition assays with gRNAs targeting HDAC6, MAPK14, PAK4, PBK, or PIM1 in four different cancer cell lines. We next designed gRNAs against the reported cancer dependencies HDAC6, MAPK14 (p38α), PAK4, PBK, and PIM1. To maximize the likelihood that a CRISPR-induced mutation results in a nonfunctional allele, guides were designed to target exons that encode key functional domains within a protein (fig. S1A) (18). We used Western blotting to verify that each guide resulted in strong protein depletion in four separate cell lines (Fig. 1D and fig. S1B), and we then further confirmed target ablation by performing a second set of Western blots with a different antibody that recognizes a distinct protein epitope (fig. S1C). Next, we conducted GFP competition assays in 32 cell lines from 12 different cancer types, which included multiple cell lines in which each gene had previously been reported to be essential (data file S1). In each experiment, four gRNAs targeting Rosa26 and AAVS1 were used as negative controls, whereas four gRNAs targeting PCNA and RPA3 were used as positive controls. These positive control guides dropped out between ~10- and ~200-fold over five passages in culture, whereas the negative control guides consistently exhibited <2.5-fold dropout. The variation in positive control dropout rates likely reflects cellular differences in Cas9 expression, proliferation, and the spectrum of indel mutations produced by the gRNA. Notably, all guides targeting HDAC6, MAPK14, PAK4, PBK, and PIM1 failed to drop out in every cell line that we tested (Fig. 1E, fig. S2, and data file S2). For instance, HDAC6 has been reported to be a genetic dependency in ARID1A-mutant ovarian cancer (6). However, in ARID1A-mutant ovarian cancer cell lines A2780, OVK18, OVTOKO, and TOV-21G, HDAC6-targeting guides failed to deplete above background levels. Similarly, PIM1 has been reported to be a genetic dependency in triple-negative breast cancer (14, 15), but PIM1-targeting guides were not depleted in any of the seven triple-negative breast cancer cell lines that we tested (data file S2). These results called into question whether these putative drug targets are required for cancer cell growth.

Generation and analysis of CRISPR-derived KO clones To further test the essentiality of these genes in cancer, we derived clones harboring CRISPR-induced KOs in each gene in multiple cancer types. All five genes were knocked out in the triple-negative breast cancer cell line MDA-MB-231 and the melanoma cell line A375. HDAC6, MAPK14, PBK, and PIM1 were knocked out in the colorectal cancer cell line DLD1, whereas PAK4 was knocked out in the colorectal cancer cell line HCT116, because it has previously been reported that PAK4 is not a dependency in DLD1 (11). To minimize the possibility that downstream translational initiation or alternative splicing bypasses the effect of a single CRISPR-induced mutation, clones were made by cotransducing cancer cells with guides that targeted two different exons in a gene of interest (Fig. 2A and fig. S1A). Complete target ablation was then verified by Western blotting using two antibodies that recognized distinct protein epitopes (Fig. 2B and figs. S3 to S5A). We next compared these KO clones to control clones transduced with guides targeting Rosa26 or AAVS1. As a positive control, we confirmed that knocking out the verified drug target MEK1 decreased proliferative capacity in A375 clones (fig. S4). However, we found that clones lacking each putative genetic dependency listed in Table 1 proliferated at levels that were indistinguishable from control A375, DLD1, and HCT116 cancer cells (Fig. 2C). For instance, PAK4-KO melanoma cells underwent an average of 20.3 population doublings over the course of 15 days in culture, compared to 19.9 doublings for the Rosa26 gRNA-transduced clones. To test whether these genes were dispensable for cell division but required for growth in other environments, we also seeded the KO clones in soft agar and assessed their ability to grow in anchorage-independent conditions. Although MEK1-KO clones formed fewer colonies in soft agar (fig. S4E), every HDAC6, MAPK14, PAK4, PBK, and PIM1 KO exhibited WT rates of colony formation, further verifying that these genes are not required for cancer cell fitness (Fig. 2, D and E). Fig. 2 Generating and analyzing single cell–derived KO clones of putative cancer dependencies. (A) Schematic of the two-guide strategy used to generate clonal KO cell lines. (B) Western blot analysis of single cell–derived A375 KO clones. ab, antibody. (C) Proliferation assays for HDAC6, MAPK14, PAK4, PBK, and PIM1 KO clones. (D) Representative images of A375 and DLD1 Rosa26 or MAPK14-KO clones grown in soft agar. Scale bar, 2 mm. (E) Quantification of colony formation in control or KO A375, DLD1, and HCT116 clones. Boxes represent the 25th, 50th, and 75th percentiles of colonies per field, and the whiskers represent the 10th and 90th percentiles. For each assay, colonies were counted in at least 15 fields under a 10× objective. Consistent with previously reported results, Rosa26 and AAVS1 control clones derived from MDA-MB-231 cell populations exhibited some variability in proliferative capacity (3, 4, 22). By analyzing a total of 12 single cell–derived control clones, we established a range of doubling times in which WT MDA-MB-231 cells can divide (fig. S5B). Every HDAC6, MAPK14, PAK4, PBK, and PIM1 KO clone proliferated at a comparable rate to these control clones (fig. S5C). All KO clones were also capable of forming colonies in soft agar at rates comparable to the control clones, further verifying that these putative dependencies are nonessential in breast cancer (fig. S5D).

Lack of homolog up-regulation in KO clones Null mutations caused by CRISPR may trigger a different cellular response than RNAi-induced gene repression, potentially contributing to the discrepancies between our results and those that had previously been reported. In particular, a recent study suggested that CRISPR-induced nonsense mutations can trigger the up-regulation of the homologs of a targeted gene, potentially compensating for the effects of the lesion (23). We assessed the expression of the closest homologs of HDAC6, MAPK14, PAK4, PBK, and PIM1 in 33 different KO clones that we generated, but we observed no consistent up-regulation of any target homolog (fig. S6). In addition, we analyzed RNA sequencing (RNA-seq) data from 10 published experiments in gene-edited cancer cells from other laboratories and similarly failed to detect consistent evidence for the up-regulation of target gene homologs (fig. S7). In several experiments, we found that the homologs of the targeted gene were down-regulated. These results suggest that homolog up-regulation is not a common consequence of CRISPR mutagenesis in human cancer cells and that compensatory homolog overexpression is unlikely to explain the lack of a detectable growth defect in the CRISPR clones that we have analyzed.

Assessing putative cancer dependencies in whole-genome CRISPR and RNAi screens Cell lines can exhibit interlaboratory variability that affects their response to different genetic and chemical perturbations (24). In addition, although we chose cancer types to study based on the dependency patterns reportedly exhibited by each gene (data file S1), it remained possible that these genes represent dependencies in a cancer lineage not included among the 32 cell lines that we studied. To test this possibility and to assess whether unique or nonrepresentative features of the cell lines used in our laboratory contributed to our discrepant results, we reanalyzed genetic dependency data from whole-genome CRISPR screens conducted in 485 cancer cell lines (fig. S8A). These screens consistently identified both pan-cancer and cell type–specific genetic dependencies (for example, Aurora B, BRAF, and PIK3CA; fig. S8, B and C). However, in accordance with our earlier results, these experiments also indicated that our chosen dependencies were fully dispensable for cancer cell fitness (fig. S8, A to C). For instance, MAPK14/p38α has previously been reported to be essential in breast cancer (9), but CRISPR screens conducted in 26 different breast cancer cell lines corroborate that its loss is tolerated without a substantial fitness defect (fig. S8D). Notably, we also reanalyzed 712 genome-wide shRNA screens, and these knockdown experiments similarly failed to identify HDAC6, MAPK14, PAK4, PBK, or PIM1 as cancer-essential genes (fig. S8, E to G). In total, these results indicate that our findings are unlikely to be explained by nonrepresentative features of the cell lines studied in our laboratory, by differences between partial and complete loss-of-function perturbations, or by these genes functioning as genetic dependencies only in certain cancer types. Instead, our data suggest that multiple genes targeted in cancer clinical trials are, in fact, fully dispensable for cancer cell growth.

Knocking down putative cancer dependencies with CRISPR interference To further investigate whether differences between partial and complete loss-of-function perturbations could explain our discrepant results, we next performed competition experiments using the CRISPR interference (CRISPRi) system. In this approach, catalytically inactive Cas9 is fused to a transcriptional repressor and targeted to a gene’s promoter, resulting in down-regulation of gene expression without the generation of a complete loss of function–inducing frameshift mutation (25). We designed three gRNAs that recognized HDAC6, MAPK14, PAK4, PBK, and PIM1 and verified that these constructs blocked the expression of their targets (fig. S9A). We then conducted competition experiments in four different cell lines, and we found that gRNAs targeting the essential replication protein MCM2 exhibited ~10- to ~20-fold dropout, whereas gRNAs targeting HDAC6, MAPK14, PAK4, PBK, and PIM1 failed to deplete (fig. S9B). These assays further verify that our results cannot be explained by the existence of different cellular responses to partial and complete loss-of-function alterations.

Assessing the sensitivity of target-KO clones to chemotherapy agents undergoing combination clinical trials Several of the proteins listed in Table 1 are currently undergoing combination clinical trials using their targeted inhibitors together with other chemotherapy agents. It is conceivable that a protein could be nonessential under normal conditions but that its loss sensitizes cells to specific chemotherapies. For instance, HDAC6 is capable of deacetylating microtubules (26), and HDAC6 inhibition has been reported to render cells vulnerable to drugs that interfere with microtubule dynamics (27). As a result of this preclinical work, two clinical trials are combining HDAC6 inhibitors with the microtubule stabilizer paclitaxel (NCT02632071 and NCT02661815). We therefore tested whether the KO clones that we had generated were sensitive to various chemotherapy agents (fig. S10, A to C). In contrast to previous results, loss of HDAC6 failed to sensitize cells to paclitaxel or to four other anticancer drugs (fig. S10A). Similarly, p38α inhibitors have been clinically applied in combination with bortezomib, gemcitabine, carboplatin, and temozolomide (NCT00087867, NCT00095680, NCT01663857, and NCT02364206), but MAPK14/p38α KO clones in multiple cell lines were as sensitive to these agents as Rosa26 control clones (fig. S10B). These results suggest that, in addition to being nonessential, these putative drug targets do not affect sensitivity to several chemotherapy agents that have been tested in combination trials.

Assessing RNAi promiscuity as a cause of the misidentification of cancer dependencies If these genes do not drive cancer growth or chemotherapy resistance, then why have inhibitors targeting the proteins that they encode been tested in human patients with cancer? A review of the literature indicates that each of these genes has been described to be essential on the basis of RNAi-induced knockdown phenotypes (data file S1). Off-target toxicity has been reported to be a common problem in the design and interpretation of RNAi-based experiments (28–30), although the impact of these issues on the therapeutic development pipeline is not known. We acquired four different RNAi constructs that were used in these prior studies and then tested their effects on the clones that we had generated. Although we were able to confirm that each construct decreased the expression of its putative target, we also found that these constructs impaired proliferation in both WT clones and clones in which the construct’s target had been knocked out (fig. S11, A to C). For example, a recent report found that PAK4-targeting siRNAs blocked cell division in HCT116 colon cancer cells and concluded that PAK4 was a genetic dependency in this cell line (31). However, we found that these same siRNAs induced an equivalent decrease in proliferation in both HCT116 PAK4-KO and HCT116 Rosa26 clones, suggesting that their effects on growth are a consequence of off-target toxicity (fig. S11A). Similarly, while knocking down PIM1 has been reported to block proliferation in the MDA-MB-231 breast cancer cell line (15), this construct had the same effect in MDA-MB-231 PIM1-KO cells (fig. S11B). Our results therefore suggest that these drug targets have advanced to clinical testing due, at least in part, to promiscuous RNAi constructs.

Assessing the specificity of cancer drugs undergoing clinical trials Off-target toxicity from small-molecule drugs can cause dangerous side effects and is a major cause of clinical trial failure (32, 33). Our results suggested that the drugs listed in Table 1 were designed to target nonessential cellular proteins, raising the possibility that the anticancer effects of these drugs could be due to off-target interactions. We therefore sought to apply CRISPR to differentiate between the on- and off-target effects of each clinical cancer drug. First, we confirmed that CRISPR could be used to verify the MOA for several genetically validated therapies. The natural product rapamycin is reported to bind to the prolyl isomerase FKBP12, and this complex inhibits the essential mTOR (mammalian target of rapamycin) kinase (fig. S12A) (34, 35). We knocked out FKBP12 using CRISPR, and we verified that these KO clones exhibited increased resistance to rapamycin treatment (fig. S12, B and C). Similarly, knocking out p53 conferred resistance to the experimental p53-activating drug nutlin-3a (fig. S12, D to F). Last, we sought to test whether CRISPR could be used to validate a published resistance-granting point mutation. We used CRISPR-mediated homology-directed repair (HDR) to introduce a missense mutation into the kinase domain of the essential mitotic kinase MPS1, and we verified that this substitution was capable of granting resistance to the small-molecule MPS1 inhibitor AZ3146 (fig. S12, G to I) (36). Thus, CRISPR-derived KO and knock-in cell lines can be used to validate on-target drug activity. Next, we applied CRISPR to interrogate the MOA of two caspase-3 activating compounds: PAC-1 and 1541B. These drugs are reported to function by catalyzing the conversion of caspase-3 from its inactive, procaspase state to its active, cleaved form, thereby causing cellular apoptosis (fig. S13A) (16, 17). Currently, PAC-1 is undergoing three different clinical trials in patients with cancer (NCT02355535, NCT03332355, and NCT03927248). We knocked out the CASP3 gene in four different cell lines and then verified protein ablation using two different antibodies (Fig. 3A and fig. S13). However, these CASP3-KO lines exhibited identical sensitivity to PAC-1 and 1541B compared with Rosa26 controls (Fig. 3B and fig. S13, D and F). These results suggest that a putative caspase-3 activator undergoing clinical trials actually kills cancer cells in a caspase-3–independent manner. Fig. 3 Target-independent cell killing by multiple anticancer drugs. (A) Western blot analysis for caspase-3 in A375 and HCT116 cells. (B) Seven-point dose-response curves of Rosa26 and CASP3-KO A375 and HCT116 cells in the presence of two putative caspase-3 activators: 1541B and PAC-1. (C) Seven-point dose-response curves of Rosa26 and HDAC6-KO A375 and DLD1 cells in the presence of two putative HDAC6 inhibitors: ricolinostat and citarinostat. (D) Seven-point dose-response curves of Rosa26 and MAPK14-KO A375 and DLD1 cells in the presence of two putative MAPK14 inhibitors: ralimetinib and SCIO-469. (E) Seven-point dose-response curves of Rosa26 and PBK-KO A375 and DLD1 cells in the presence of two putative PBK inhibitors: OTS514 and OTS964. (F) Seven-point dose-response curves of Rosa26 and PIM1-KO A375 and DLD1 cells in the presence of a putative PIM1 inhibitor: SGI-1776. (G) Seven-point dose-response curves of Rosa26 and PAK4-KO A375 and HCT116 cells in the presence of a putative PAK4 inhibitor: PF-3758309. We next tested each putative HDAC6, MAPK14, PAK4, PBK, and PIM1 inhibitor in control and KO clones. If these drugs act by specifically inhibiting their reported targets, then cancer cells that totally lack the expression of their targets would be expected to be resistant to these drugs’ effects. In contrast, if a drug kills cells in which its reported target has been knocked out, then this drug necessarily kills cells by affecting another protein or proteins. In every instance that we tested, cancer cells in which HDAC6, MAPK14, PAK4, PBK, or PIM1 had been knocked out exhibited WT sensitivity to their putative targeted inhibitors (Fig. 3 and fig. S14, A and B). For example, we found that the PAK4 inhibitor PF-3758309 blocked the growth of both Rosa26 and PAK4-KO melanoma cells with a GI 50 value of ~9 nM (Fig. 3G). Given that this drug is fully capable of killing cells in which its putative target has been deleted, the ability of PF-3758309 to block cancer cell growth must be through an off-target effect. To further interrogate whether the drugs studied in this manuscript could exhibit an on-target MOA in an additional genetic background, we knocked out HDAC6 in TOV-21G cells, an ARID1A-mutant ovarian cancer cell line in which this gene has been reported to be a dependency (fig. S15A) (6). However, TOV-21G HDAC6-KO cells exhibited WT fitness in vitro and in soft agar (fig. S15, B and C), and these cells remained sensitive to citarinostat and ricolinostat, two putative HDAC6 inhibitors in clinical trials (fig. S15D). In total, all 10 different anticancer agents targeting CASP3, HDAC6, MAPK14, PAK4, PBK, or PIM1 exhibited clear evidence of target-independent cell killing in every KO cell line that we examined. Last, we applied these putative inhibitors to investigate several combination chemotherapy trials. As described above, HDAC6 inhibitors are currently undergoing testing in cancer patients along with paclitaxel, and p38α inhibitors have also been combined with several different therapeutic agents. We verified that cotreatment with a targeted inhibitor and a second agent generally caused a greater decrease in cancer cell viability than either agent alone (fig. S14B). However, this synthetic enhancement was observed in both Rosa26 and target-KO clones, suggesting that these additive effects are also due to an off-target interaction.

Discovering the true target of OTS964 If these clinical anticancer therapies do not kill cells by inhibiting their reported targets, then how do they block cancer growth? We note that, although the MOA of each drug has previously been characterized using biochemical and biophysical approaches, there is little genetic evidence linking each drug to its reported target. We hypothesized that an alternative genetic methodology could shed light on the true target of a therapeutic agent whose MOA was in question. For this work, we chose to focus on the putative PBK inhibitor OTS964, because it exhibited nanomolar potency in multiple cancer types and our CRISPR experiments had provided clear evidence that PBK was not required for cell proliferation. Moreover, OTS964 has been reported to affect mitotic progression (13), and antimitotic drugs have been historically proven to be highly successful anticancer agents (37). To identify mutations that conferred resistance to OTS964, we used HCT116 colorectal cancer cells, which harbor an increased mutation rate caused by a defect in mismatch repair (38). We cultured HCT116 cells in the presence of a lethal concentration of OTS964 and successfully isolated 12 clones that were capable of growing under these conditions (Fig. 4A). We found that these clones exhibited stable resistance to OTS964, as they failed to revert to OTS964 sensitivity after prolonged growth in normal medium (fig. S16A). Cancer cells commonly acquire chemotherapy resistance by amplifying the P-glycoprotein drug efflux pump (39). However, the OTS964-resistant clones remained sensitive to paclitaxel, a verified P-glycoprotein substrate, suggesting that they had not acquired a multidrug resistance phenotype (fig. S16B) (40). These experiments indicated that our drug-resistant clones could harbor a mutation or mutations that specifically altered OTS964 sensitivity. Fig. 4 Discovery of CDK11 as the in cellulo target of the mischaracterized anticancer drug OTS964. (A) A schematic of the strategy to use the highly mutagenic HCT116 cell line to isolate mutations that confer OTS964 resistance. (B) Sanger sequencing validation of two heterozygous mutations in the CDK11B kinase domain. (C) Constructs used to introduce the G579S mutation into CDK11B via CRISPR-mediated HDR. Yellow arrowhead indicates the site of Cas9 cleavage. Red bar indicates the G579S substitution, and blue bars indicate silent mutations introduced to prevent recutting after HDR. (D) Crystal violet staining of cancer cells transfected with the indicated constructs and then cultured in a lethal concentration of OTS964. (E) Seven-point dose-response curves of Rosa26, PBK-KO, and CDK11BG579S clones grown in varying concentration of OTS964. (F) Titration experiments reveal that OTS964 binds to CDK11B with a K D of 40 nM. (G) Pancreatic cancer cell line MiaPaca-2 was transduced with guides specific for CDK11A, guides specific for CDK11B, or guides that harbored cut sites in both genes. (H) A375 H2B-mCherry cells (left) or A375 H2B-mCherry cells that express CDK11BG579S (right) were arrested at G 1 /S with a double-thymidine block and then were released into normal medium or medium containing OTS964. The percentage of mitotic cells in each population was scored every hour. (I) Representative images of the experiments in (H), 9 hours after release from thymidine. Scale bar, 25 μm. To identify genetic alterations capable of conferring OTS964 resistance, we subjected 10 OTS964-resistant clones, 1 Rosa26 control clone, and the parental cell population to whole-exome sequencing (WES). Notably, all 10 resistant clones were found to harbor heterozygous missense mutations in the poorly characterized cyclin-dependent kinase (CDK) CDK11B (fig. S16C). Eight clones harbored two mutations in this gene, H572Y and G579S, in trans, whereas two clones harbored only the G579S substitution. No CDK11B mutations were observed in the parental population or in the Rosa26 control clone. Sanger sequencing verified the presence of the CDK11B mutations in two independent drug-resistant clones that were not subjected to WES (Fig. 4B and fig. S16C). These mutations were also absent from additional control clones that were analyzed (fig. S16C) and have not been previously observed in the Catalog of Somatic Mutations in Cancer database (41). The human genome encodes two CDK11 proteins, CDK11A and CDK11B, that are 97% identical and that arose from an evolutionarily recent gene duplication event (42). The CDK11 family has been reported to support various cellular processes, including transcription, splicing, and chromosome segregation (43), but its role in cancer is unknown. No drugs have previously been reported to target CDK11, and inhibitors that are specific for single CDKs are difficult to discover due to the sequence similarity among these kinases (44). We aligned the sequences of the human CDKs, and we noted that 19 of 20 of these proteins harbored an alanine residue immediately upstream of the magnesium-coordinating DFG motif (fig. S16D). Only CDK11 contained a glycine at this location, and this glycine was mutated to serine in every OTS964-resistant clone that we sequenced (fig. S16, C and D). This amino acid position (called “xDFG”) has previously been identified as a key residue that affects kinase inhibitor binding (45), suggesting a potential basis for CDK11-selective inhibition. To test whether the xDFG Gly→Ser mutation was sufficient to confer resistance to OTS964, we designed a strategy to use CRISPR-mediated HDR to introduce this substitution into the endogenous CDK11B gene (Fig. 4C). These experiments revealed that this point mutation was sufficient to restore viability in A375, A2780, DLD1, and MDA-MB-231 cancer cells grown in a lethal concentration of OTS964 (Fig. 4, D and E). To verify that our results were not an off-target effect of CRISPR, we generated a retrovirus to stably express CDK11BG579S cDNA, and we confirmed that this construct was also sufficient to confer OTS964 resistance (fig. S16E). In an HCT116 clone that had spontaneously evolved resistance to OTS964, eliminating mutant CDK11B with CRISPR restored OTS964 sensitivity, demonstrating that this alteration is both necessary and sufficient for drug resistance (fig. S16F). Introducing an alanine substitution into residue 579, so that the CDK11B xDFG motif was identical to the other human CDKs, was also sufficient to decrease the efficacy of OTS964 (fig. S16, G and H). Last, to confirm a direct interaction between OTS964 and CDK11B, we assessed the binding of OTS964 to different CDKs. OTS964 bound to CDK11B with a K D (dissociation constant) of 40 nM, and it displayed greater than 10-fold selectivity for this kinase compared with several other CDKs (Fig. 4F and fig. S16I). In total, these results indicate that the putative PBK inhibitor OTS964 actually functions by targeting CDK11, and its specificity for this kinase is conferred by CDK11’s distinct xDFG motif.