Cancer kinome sequencing studies have identified several protein kinases predicted to possess driver (i.e., causal) mutations. Using bioinformatic applications, we have pinpointed DAPK3 (ZIPK) as a novel cancer-associated kinase with functional mutations. Evaluation of nonsynonymous point mutations, discovered in DAPK3 in various tumors (T112M, D161N, and P216S), reveals that all three mutations decrease or abolish kinase activity. Furthermore, phenotypic assays indicate that the three mutations observed in cancer abrogate the function of the kinase to regulate both the cell cycle and cell survival. Coexpression of wild-type (WT) and cancer mutant kinases shows that the cancer mutants dominantly inhibit the function of the WT kinase. Reconstitution of a non–small cell lung cancer cell line that harbors an endogenous mutation in DAPK3 (P216S) with WT DAPK3 resulted in decreased cellular aggregation and increased sensitivity to chemotherapy. Our results suggest that DAPK3 is a tumor suppressor in which loss-of-function mutations promote increased cell survival, proliferation, cellular aggregation, and increased resistance to chemotherapy. Cancer Res; 71(8); 3152–61. ©2011 AACR.

Sequencing technologies are evolving at a rapid rate resulting in decreasing costs associated with sequencing a single genome. Estimates predict that the cost of sequencing a single genome will drop significantly and exome sequencing costs are already estimated at $5,000, making it feasible that cancer patients may soon be able to have both their normal and cancer genomes sequenced (1). Comparison of these genomes will provide a snapshot of the genetic aberrations that occurred during the evolution of a cancer (2, 3). Although the sequencing of a patient's normal and cancer genome is becoming feasible, our understanding of the many genetic aberrations that contribute to various cancer phenotypes lags far behind the development and application of this technology. If this information is to become clinically relevant we must understand and catalog the functional consequences of the observed genetic changes. Understanding both the functional consequences of genetic changes and the signaling networks they impact will be essential if this information is to be used as a guide for therapeutic treatment (4).

In an effort to identify protein kinases with candidate cancer driver mutations, we used bioinformatic tools to analyze kinases with somatic mutations reported in a cancer kinome sequencing study performed by the Sanger Centre that evaluated 518 kinases in 210 cancers (5–8). We identified DAPK3 as a strong candidate to possess functional cancer-associated mutations. DAPK3 (also called ZIPK) is a member of the DAPK (death-associated protein kinase) family (9), and in addition to the N-terminal catalytic domain, this kinase has a leucine zipper domain and 2 putative NLS (10). DAPK3 is proapoptotic (11) and is proposed to be a tumor suppressor, suggesting that mutations in DAPK3 could result in loss of function. Consistent with this notion, the gene for DAPK1, which has 83% homology with DAPK3 in the kinase domain (9), is frequently methylated in cancers, and DAPK1 is considered a tumor suppressing kinase (12–14). All 3 observed cancer mutations in DAPK3 are at residues conserved in the other 2 DAPK family members, DAPK1 and DAPK2 (Fig. 1A). D161 is in the DFG motif, and its mutation to Asn would be expected to greatly reduce catalytic activity; on the basis of the DAPK3 catalytic domain structure (15), T112 is in a surface accessible loop at the start of the C-lobe, and P216 lies in an interhelix loop largely buried in the C-lobe (Fig. 1B). The DAPK3 mutations are heterozygous and each cancer patient retains a wild-type (WT) allele. The DAPK3 gene is mutated at a frequency of 3.2% in the panel of lung, ovarian, and colon cancers examined (the frequency of mutations was 1.4% in all cancers examined; 8). The T112M and D161N mutations were identified in the primary tumors of a colon cancer and ovarian cancer patient, respectively, and no DAPK3 mutations were identified in normal cells from the same patients. The P216S mutation was identified in the H1770 non–small cell lung cancer (NSCLC) cell line and no DAPK3 mutations were observed in the pair-matched normal cells (NCI-BL1770–available through ATCC).

Figure 1.

Mutational analysis and biochemical characterization of DAPK3 cancer variants. A, sequence alignment of DAPK family members highlighting conservation of mutated residues in all 3 family members. Yellow boxes indicate amino acids mutated in cancer. B, crystal structure of DAPK3 indicating where the mutations occurred. C, CanPredict analysis to show how this enhances the selection of putative cancer-associated kinases, the kinase RIPK1, which is predicted to have a driver mutation is not predicted to have a cancer-related mutation and therefore is not a good candidate for further investigation. In contrast, evaluation of somatic mutations of DAPK3 (also predicted to possess a driver mutation; 99%) reveal that all 3 somatic mutations are predicted to be cancer related. D, H157 and 293T cells were transfected with vector or indicated FLAG-tagged DAPK3 constructs for 48 hours under high-serum conditions prior to lysis. Immunoblots of lysates from H157 NSCLC or 293T cells were probed with antibodies to phospho-MLC2 (P-MLC2), MLC2, ZIPK, or anti-FLAG. E, H157 cells were transfected with vector or FLAG-tagged DAPK3 constructs as indicated under high-serum conditions (10% FBS DMEM); thereafter, FLAG-DAPK3 was immunoprecipitated and incubated with MLC2 generated from bl-21 bacterial cells. MLC2 phosphorylation was detected by using phospho-specific MLC2 (Thr18/Ser19) antibodies.

Figure 1.

Mutational analysis and biochemical characterization of DAPK3 cancer variants. A, sequence alignment of DAPK family members highlighting conservation of mutated residues in all 3 family members. Yellow boxes indicate amino acids mutated in cancer. B, crystal structure of DAPK3 indicating where the mutations occurred. C, CanPredict analysis to show how this enhances the selection of putative cancer-associated kinases, the kinase RIPK1, which is predicted to have a driver mutation is not predicted to have a cancer-related mutation and therefore is not a good candidate for further investigation. In contrast, evaluation of somatic mutations of DAPK3 (also predicted to possess a driver mutation; 99%) reveal that all 3 somatic mutations are predicted to be cancer related. D, H157 and 293T cells were transfected with vector or indicated FLAG-tagged DAPK3 constructs for 48 hours under high-serum conditions prior to lysis. Immunoblots of lysates from H157 NSCLC or 293T cells were probed with antibodies to phospho-MLC2 (P-MLC2), MLC2, ZIPK, or anti-FLAG. E, H157 cells were transfected with vector or FLAG-tagged DAPK3 constructs as indicated under high-serum conditions (10% FBS DMEM); thereafter, FLAG-DAPK3 was immunoprecipitated and incubated with MLC2 generated from bl-21 bacterial cells. MLC2 phosphorylation was detected by using phospho-specific MLC2 (Thr18/Ser19) antibodies.

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Our results indicate that all 3 mutations in DAPK3 decrease or abolish the function of the kinase and render it unable to regulate cell cycle progression and cell survival. Furthermore, we observe that the cancer mutants can suppress the function of the WT kinase, suggesting that these mutants can act in a dominant-negative fashion. Reconstitution of the DAPK3 pathway in a cancer cell line harboring an endogenous mutation in DAPK3 results in loss of cellular aggregation and increased sensitivity to chemotherapy.

Materials

DAPK3-specific SMARTpool siRNA was purchased from Dharmacon and targeted the following sequences in DAPK3: 5′-ccacgcgtctgaaggagta-3′ (si-1); 5′-gatcccaagcggagaatga-3′ (si-2); 5′-ggacgtggaggaccattat-3′ (si-3); and 5′-gaacgtgcgtggtgaggac-3′ (si-4). Single DAPK3 siRNA targeted the following sequences: 5′-acgacatcttcgagaacaa-3′ (siDAPK3-1), 5′-cagccaagttcatcaagaa-3′ (siDAPK3-2), and 5′-acatcatgctggtggacaa-3′ (siDAPK3-3). The following phospho-specific antibodies were purchased from Cell Signaling: P-MLC2 (Thr18/Ser19) and P-histone H3 (Thr11). DAPK3 (ZIPK) antibody was purchased from ProSci Incorporated. MLC2 antibody was purchased from Santa Cruz (sc-28329) and anti-FLAG monoclonal antibody was purchased from Sigma (F3165). Propidium iodide and etoposide were both purchased from Sigma. Cell lines were originally purchased from ATCC, and were authenticated by the ATCC cell biology program and were not passaged for more than 6 months before bringing new cells out of freeze or purchasing a new cell aliquot from the ATCC. The H157 NSCLC cell line was a gift from Dr. Phillip Dennis, National Cancer Institute, and was established at the NCI/Navy Medical oncology.

Cloning and expression

3X-FLAG full-length DAPK3 cDNA, T180A, and Δ273 were provided by Dr. Timothy Haystead (Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC). HA-DAPK3 was provided by Dr. Kevan Shokat (Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA). The cancer mutant DAPK3 variants were generated by converting the nucleotide at position 335 from a C to a T (T112M), nucleotide at position 481 from a G to an A (D161N), and nucleotide at position 646 from a C to a T (P216S) by using QuikChange site-directed mutagenesis kit (Stratagene). These variants were identical to the somatic variants observed in cancer patients or in the NSCLC cell line H1770 (P216S). Glutathione S-transferase (GST)-tagged myosin light chain for bacterial expression, used for in vitro DAPK3 kinase assays, was a gift from Dr. Ruey-Hwa Chen (Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan).

Cell transfections and immunoblotting

The H1770 NSCLC cell line was maintained in RPMI 1640 (Cellgro), and all other cell lines were maintained in Dulbecco's modified Eagle's medium (DMEM; Cellgro); both media were supplemented with 10% FBS and 1% penicillin/streptomycin. Cells were maintained at 37°C in 5% CO2. Transient transfections of all cell types were carried out by using Effectene reagents (Qiagen). Lipofectamine 2000 (Invitrogen) was used to transfect siRNAs into all cells. Transient transfections and siRNA experiments were performed as previously described (16), except for H1770 cells. H1770 cells were pipetted repeatedly to break up cell aggregates and transfected immediately as previously described (16). Transfection efficiencies for 293T and H157 cell lines averaged between 70% and 90% for each experiment; efficiencies for all other cell lines averaged between 50% and 80%. For immunoblotting, transfected cells were lysed in buffer 1 at room temperature [50 mmol/L Na2HPO4 (pH 7.5), 1 mmol/L sodium pyrophosphate, 20 mmol/L NaF, 2 mmol/L EDTA, 2 mmol/L EGTA, 1% SDS, 1 mmol/L DTT, 200 μmol/L benzamidine, 40 μg/mL leupeptin, and 1 mmol/L phenylmethylsulfonyl fluoride (PMSF)] and sonicated for 4 seconds. Lysates containing equal amounts of protein were analyzed on SDS-PAGE gels, and individual blots were probed by using each antibody.

Kinase assays and coimmunoprecipitations

GST-MLC2 was expressed in BL21 bacterial cells and kinase assays were performed by immunoprecipitating FLAG-DAPK3 from cells and incubating with GST-MLC2 as a substrate. The activity of full-length FLAG-DAPK3 and other indicated variants was assessed by expressing and immunoprecipitating FLAG-DAPK3 from H157 cell lysates. Cells were lysed in buffer 2 [20 mmol/L HEPES (pH 7.4), 1% Triton X-100, 1 mmol/L DTT, 200 μmol/L benzamidine, 40 μg/mL leupeptin, 1 mmol/L PMSF], sonicated for 3 seconds, and precleared. Detergent soluble lysates (buffer 2) were incubated overnight at 4°C with FLAG antibody and UltraLink Protein A/G beads (Pierce). Beads were then washed 3 times with buffer 1 and incubated in kinase buffer [25 mmol/L Tris (pH 7.5), 5 mmol/L β-glycerophosphate, 2 mmol/L DTT, 0.1 mmol/L Na3VO4, 10 mmol/L MgCl2], and 200 μmol/L ATP with purified GST-MLC2, 1 μg per reaction in 50 μL kinase reaction buffer. Kinase reactions were performed at 30°C for 25 minutes, terminated by the addition of sample buffer and phosphorylation was detected by using the P-MLC2 antibody (Thr18/Ser19).

Proliferation and apoptosis assays

For apoptotic assays, cells were switched to low-serum media conditions (0.1% FBS) and transfected with the indicated DAPK3 constructs for 48 hours. Floating cells were collected and adherent cells were harvested by trypsinization and then centrifuged at 1,000 × g for 5 minutes. Cells were fixed in ice-cold 70% methanol, added drop wise, and then incubated at 20°C for 30 minutes. Cells were centrifuged and incubated with propidium iodide (25 μg/mL) supplemented with RNase A (30 μg/mL) for 30 minutes at room temperature. Quantification of sub-2N DNA in apoptosis assays was determined by flow cytometry analysis by using a Becton Dickinson FACSort and by manual gating by using CellQuest software. Gating was performed on blinded samples. For all cell lines, apoptotic assays were performed on whole cell population. To determine G1/S ratios, cells were transfected with FLAG-DAPK3 constructs and incubated under high-serum conditions (10% FBS) for 48 hours. For bromodeoxyuridine (BrdUrd) incorporation assays, cells were maintained in high growth media (10% FBS) and transfected DAPK3 constructs, and incubated for 48 hours prior to performing assays following the manufacturer's protocol (Calbiochem, QIA58). For DAPK3 siRNA-depleted cells, BrdUrd incorporation assays were performed on cells incubated under low-serum conditions (0.1% FBS) and transfected with 100 nmol/L SMARTpool siRNA for 48 hours.

Statistical and cell aggregation analysis

Statistical comparison of values was performed by the Student t test comparing empty vector controls or untreated cells to indicated DAPK3 constructs. Quantification of cellular aggregation was determined by Image J software and spheroid size was correlated to the number of pixels in an area of a given spheroid. Eight cellular aggregates were included in each measurement.

To determine whether mutations identified in DAPK3 are likely to alter the protein function, we used the CanPredict (http://www.cgl.ucsf.edu/Research/genentech/canpredict/) web application to determine whether the mutations are likely to be cancer-associated mutations (6). This web application employs an algorithm based on 3 measurements (SIFT, LogR.E-vlaue, and GOSS values) to determine whether a given mutation is a probable cancer mutation (6). To show how this analysis could enhance the selection of potential cancer-associated kinases, evaluation of mutations in DAPK3 by CanPredict reveals that all 3 somatic mutations are predicted to be cancer-related, and therefore we considered this kinase to be an excellent candidate for further investigation (Fig. 1C; at least one mutation in DAPK3 is also predicted to be a driver mutation based on biostatistical analysis; ref. 8). These results were verified by PMUT (5), and all 3 DAPK3 mutations were predicted to be driver mutations (pathologic). To show that many of the mutations analyzed by CanPredict are suggested to be passenger mutations, we have included the analysis of RIPK1 (Fig. 1C).

We generated the 3 observed DAPK3 somatic mutations—T112M (colon cancer), D161N (ovarian cancer), and P216S (NSCLC)—in full-length DAPK3 cDNA by using site-directed mutagenesis. FLAG-tagged versions were expressed in the H157 NSCLC cell line, which was selected for initial studies because it is amenable to transfection and is sensitive to apoptotic stimuli, and 293T cells. To assess kinase activity, we monitored phosphorylation of MLC2, a reported DAPK3 substrate (17), at T18/S19 by blotting with anti-pT18/pS19 antibodies. Expression of WT DAPK3 increased MLC2 phosphorylation (Fig. 1D), whereas expression of D161N, T112M, and P216S did not. These data suggest that all 3 somatic mutations inactivate DAPK3, because overexpression of the DAPK3 cancer mutants did not increase MLC2 phosphorylation. As a positive control we expressed the constitutively active Δ273 DAPK3 mutant (ZIPKΔ273), which is a C-terminal truncation variant that consists of only the kinase domain and was previously reported to have increased kinase activity (18). As a negative control we expressed T180A DAPK3 (mutated at the key activation loop autophosphorylation site), which was reported to have decreased kinase activity (18). We complemented these studies by carrying out in vitro kinase assays with DAPK3 immunoprecipitated from H157 cells by using recombinant GST-MLC2 purified from bacteria cells as a substrate (Fig. 1E). Consistent with the in vivo results, D161N, P216S, and T180A DAPK3 had very low kinase activity compared with WT DAPK3; T112M had significantly decreased activity. Combined, these data indicate that DAPK3 mutations identified in cancer patients significantly suppress the activity of the kinase.

To determine whether the DAPK3 cancer mutations alter the function of the WT kinase, we transiently expressed them in H157 cells (transfection efficiency is 70%–90%, based on gating green fluorescent protein–positive cells, and overexpression of WT DAPK3 is approximately 3-fold based on Image J densitometry) and monitored cell survival and cell cycle progression of the whole cell population. Overexpression of WT DAPK3 under high-serum (10% FBS) media conditions caused cells to accumulate in G1, resulting in an increase in the G1/S ratio (Fig. 2A). This phenotype was more pronounced with DAPK3 Δ273, but all 3 cancer variants of DAPK3, as well as T180A were ineffective at inhibiting cell cycle progression. To determine whether this inhibition of cell cycle progression translated into a decrease in cellular proliferation we overexpressed DAPK3 constructs in H157 cells and monitored BrdUrd incorporation. Consistent with the cell cycle analysis, overexpression of WT DAPK3 and the constitutively active Δ273 DAPK3 significantly decreased BrdUrd incorporation (Fig. 2B), whereas the DAPK3 T180A mutant and all 3 cancer mutants did not (Fig. 2B). These data are consistent with a model in which loss of functional growth inhibitory DAPK3 may increase cell proliferation, a hallmark of tumorigenesis.

Figure 2.

Mutations in DAPK3 result in loss-of-function. A, H157 NSCLC cells were transfected with indicated DAPK3 variants or vector alone, under high-serum conditions (10% FBS DMEM) for 48 hours and apoptosis (sub-2N DNA content) and G1/S ratios were determined by propidium iodide incorporation assays and flow cytometry. An increase was observed in the G1/S ratio in cells transfected with FLAG-DAPK3 or FLAG-Δ273 compared with cells transfected with vector alone, T180A, or cancer variants. *, P < 0.01. B, overexpression of FLAG-DAPK3 or FLAG-Δ273 for 48 hours under high-serum conditions decreased BrdUrd incorporation in H157 cells, whereas T180A or cancer mutants did not significantly alter BrdUrd incorporation. *, P < 0.05. C, depletion of DAPK3 for 48 hours under high-serum conditions increased BrdUrd incorporation in H157 and U87 cells. Immunoblots were performed in parallel to ensure that DAPK3 was being adequately depleted and we observed a concomitant decrease in MLC2 and histone H3 phosphorylation. *, P < 0.05. D, depletion of DAPK3 with 3 unique siRNAs for 48 hours under high-serum conditions decreased the G1/S ratio in the H157 cells. Immunoblots were performed in parallel to ensure that DAPK3 was being depleted. E, H157 cells were transfected with indicated constructs for 48 hours under low-serum conditions. Histograms show sub-2N DNA; quantitation of sub-2N DNA is indicated in bar graph. *, P < 0.01. A–E, the data in the bar graphs are representative of assays performed in triplicate, with error bars indicating SD, and are representative of 3 independent experiments.

Figure 2.

Mutations in DAPK3 result in loss-of-function. A, H157 NSCLC cells were transfected with indicated DAPK3 variants or vector alone, under high-serum conditions (10% FBS DMEM) for 48 hours and apoptosis (sub-2N DNA content) and G1/S ratios were determined by propidium iodide incorporation assays and flow cytometry. An increase was observed in the G1/S ratio in cells transfected with FLAG-DAPK3 or FLAG-Δ273 compared with cells transfected with vector alone, T180A, or cancer variants. *, P < 0.01. B, overexpression of FLAG-DAPK3 or FLAG-Δ273 for 48 hours under high-serum conditions decreased BrdUrd incorporation in H157 cells, whereas T180A or cancer mutants did not significantly alter BrdUrd incorporation. *, P < 0.05. C, depletion of DAPK3 for 48 hours under high-serum conditions increased BrdUrd incorporation in H157 and U87 cells. Immunoblots were performed in parallel to ensure that DAPK3 was being adequately depleted and we observed a concomitant decrease in MLC2 and histone H3 phosphorylation. *, P < 0.05. D, depletion of DAPK3 with 3 unique siRNAs for 48 hours under high-serum conditions decreased the G1/S ratio in the H157 cells. Immunoblots were performed in parallel to ensure that DAPK3 was being depleted. E, H157 cells were transfected with indicated constructs for 48 hours under low-serum conditions. Histograms show sub-2N DNA; quantitation of sub-2N DNA is indicated in bar graph. *, P < 0.01. A–E, the data in the bar graphs are representative of assays performed in triplicate, with error bars indicating SD, and are representative of 3 independent experiments.

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To our knowledge this was the first time DAPK3 has been implicated in regulating cell cycle progression and proliferation. To verify that endogenous DAPK3 regulates the cell cycle we depleted endogenous DAPK3 from H157 NSCLC cells and U87 glioma cells, which both have WT DAPK3, using SMARTpool siRNA transfection. Immunoblots showed that the level of endogenous DAPK3 was significantly reduced, correlating with a decrease in phosphorylation of downstream substrates, including histone H3 (T11; ref. 19) and MLC2 (Thr18/Ser19; Fig. 2C, left). In parallel, we measured relative BrdUrd incorporation in cells depleted of DAPK3, and found that this increased BrdUrd incorporation in both H157 and U87 cells (Fig. 2C, right), consistent with DAPK3 regulating both cell cycle progression and cell proliferation. To verify that the observed effect was not due to off-target effects, we depleted DAPK3 in H157 cells by using DAPK3-specific single siRNA oligos. As displayed in Figure 2D, depletion of DAPK3 in the H157 cells correlated with a decrease in MLC2 phosphorylation as well as a decrease in the G1/S ratio indicating the cells are progressing through the cell cycle at an increased rate (Fig. 2D).

To determine whether DAPK3 regulated cell survival, we overexpressed DAPK3 constructs under low-serum conditions (0.1% FBS) and monitored apoptosis. Under these conditions, expression of WT DAPK3 caused a greater than 2-fold increase in apoptosis, whereas expression of T112M, D161N, and P216S DAPK3 did not, again suggesting that these mutants are not functional (Fig. 2E; a similar increase in apoptosis is observed in these cells when treated with other cytotoxic agents such as etoposide, cis-platinum, or taxol; ref. 20).

All the DAPK3 mutations identified to date in cancer patients are heterozygous, suggesting that a loss-of-function mutation in one allele could suppress the function of the WT allele by preventing activation of the WT allele following dimerization between an inactive DAPK3 cancer mutant and the WT DAPK3 proteins (15, 18). To determine whether these cancer mutants did indeed suppress the function of WT kinase, we first evaluated endogenous MLC2 phosphorylation in several cancer cell lines with high levels of phospho-MLC2 (T18/S19). Overexpression of WT DAPK3 (2- to 5-fold overexpression compared with endogenous DAPK3 based on Image J densitometry for the 3 cell lines examined) did not significantly increase MLC2 phosphorylation, although Δ273 DAPK3 did (Fig. 3A). Consistent with the notion that the DAPK3 cancer mutants may suppress endogenous WT DAPK3 function, overexpression of all 3 cancer mutants as well as the T180A DAPK3 suppressed MLC2 phosphorylation. To verify that mutant endogenous DAPK3 could suppress WT DAPK3, we coexpressed the 3 cancer mutants individually with WT DAPK3 in H157 NSCLC cells and monitored phosphorylation of MLC2 (Fig. 3B). Overexpression of WT DAPK3 increased MLC2 phosphorylation and this was blocked by coexpression of each of the cancer mutants when transfected at equal input DNA level (Fig. 3B). To verify that the cancer mutants acted through association with WT DAPK3, we performed pull-down experiments with transiently coexpressed HA-tagged WT DAPK3 and FLAG-tagged WT DAPK3 or FLAG-tagged DAPK3 cancer mutants. Cancer mutants associated with WT DAPK3 to a level comparable to the WT DAPK3 (FLAG; Fig. 3C). Together these data suggest that the cancer mutants can act in a dominant manner to suppress the activity of the WT allele.

Figure 3.

DAPK3 cancer mutants inhibit the activity of the WT kinase. A, A549, HeLa, and U87 cells were transfected with the indicated constructs for 48 hours under high-serum conditions prior to lysis. The phosphorylation state of MLC2 in lysates was detected by immunoblot analysis. B, coexpression of cancer mutants suppresses the activity of WT DAPK3. H157 cells were cotransfected with the indicated constructs for 48 hours under high-serum conditions and MLC2 phosphorylation was detected by immunoblot analysis. C, cancer mutants associate with WT DAPK3. H157 cells were cotransfected with the indicated constructs for 48 hours under high-serum conditions, lysed, immunoprecipitated overnight at 4°C, and association was detected by immunoblot analysis.

Figure 3.

DAPK3 cancer mutants inhibit the activity of the WT kinase. A, A549, HeLa, and U87 cells were transfected with the indicated constructs for 48 hours under high-serum conditions prior to lysis. The phosphorylation state of MLC2 in lysates was detected by immunoblot analysis. B, coexpression of cancer mutants suppresses the activity of WT DAPK3. H157 cells were cotransfected with the indicated constructs for 48 hours under high-serum conditions and MLC2 phosphorylation was detected by immunoblot analysis. C, cancer mutants associate with WT DAPK3. H157 cells were cotransfected with the indicated constructs for 48 hours under high-serum conditions, lysed, immunoprecipitated overnight at 4°C, and association was detected by immunoblot analysis.

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To determine whether the DAPK3 cancer mutants could suppress the function of the WT kinase, we coexpressed T112M, D161N, or P216S DAPK3 with WT DAPK3 in H157 NSCLC cells and monitored cell cycle progression. Coexpression of the cancer mutants caused a reduction in the G1/S ratio observed when WT DAPK3 is expressed alone (Fig. 4A). This suggests that the cancer mutants interfere with the ability of WT DAPK3 to slow cell cycle progression. Consistent with these results, coexpression of DAPK3 cancer mutants with WT DAPK3 suppressed apoptosis induced by WT DAPK3 (Fig. 4B). This provides further evidence that the cancer-associated mutations in DAPK3 exert a dominant-negative effect over WT DAPK3 function.

Figure 4.

DAPK3 cancer mutants inhibit the function of the WT kinase. A and B, coexpression of cancer mutants suppresses the function of WT DAPK3. H157 cells were cotransfected with indicated constructs for 48 hours under high-serum (10% FBS; cell cycle analysis) or low-serum (0.1% FBS; apoptosis analysis) conditions. Apoptosis (sub-2N DNA content) and G1/S ratios were determined by propidium iodide incorporation assays and flow cytometry.

Figure 4.

DAPK3 cancer mutants inhibit the function of the WT kinase. A and B, coexpression of cancer mutants suppresses the function of WT DAPK3. H157 cells were cotransfected with indicated constructs for 48 hours under high-serum (10% FBS; cell cycle analysis) or low-serum (0.1% FBS; apoptosis analysis) conditions. Apoptosis (sub-2N DNA content) and G1/S ratios were determined by propidium iodide incorporation assays and flow cytometry.

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An important goal of this research is to address the functional relevance of mutations in cancer-associated kinases in the context of the cancer in which they occur. To this end, we used a cell line, H1770, with an endogenous mutation in DAPK3 that occurred in a NSCLC patient, and was present in only the tumor cells. H1770 cells harbor a P216S mutation in DAPK3, which we have shown is a loss-of-function mutation (this cell line is heterozygous WT/P216S). Sequencing of DAPK3 RNA verified that H1770 cells carry the P216S endogenous mutation, and the sequencing chromatograms suggest that both alleles are expressed at equal levels (not shown). This NSCLC cell line is a suspension cell line that grows as large cellular aggregates (Fig. 5A). Depletion of total DAPK3 from these cells with SMARTpool siRNA did not decrease MLC2 or histone H3 phosphorylation (levels of phosphorylation for these proteins were already very low and barely detectable with the antibodies; Fig. 5C), or alter cell cycle distribution, suggesting that DAPK3 and downstream signaling are inactive in this cell line (data not shown). Reconstitution of these cells with WT DAPK3 resulted in decreased cellular aggregation (Fig. 5A; quantified in Fig. 5B) and an increase in MLC2 phosphorylation (Fig. 5C). Similar results were observed in cells expressing Δ273 DAPK3 (Fig. 5A and 5C), whereas no significant changes were observed in cells expressing P216S DAPK3. This suggests that loss of DAPK3 may enhance cellular aggregation and cell-to-cell adherence. Because cell-to-cell adhesion can promote chemotherapeutic resistance, we next determined whether reconstitution of H1770 cells with WT DAPK3 rendered them more sensitive to chemotherapy. As shown in Figure 5D, cells expressing WT DAPK3 were somewhat more sensitive to etoposide than cells transfected with empty vector or P216S DAPK3. These data underscore the importance of the P216S mutation in this cell line, as reactivation of the DAPK3 pathway decreased cellular aggregation and rendered these cells more sensitive to apoptosis.

Figure 5.

LOF mutation in DAPK3 promotes cell–cell adhesion and chemotherapeutic resistance. A, reconstitution of DAPK3 pathway in H1770 cells that harbor a P216S mutation in DAPK3 reduces cell–cell adhesion. H1770 cells were transfected with the indicated constructs for 48 hours under high-serum conditions and cells were photographed. White bar, 0.2 mm. B, quantification of cellular aggregates is displayed and was determined by using Image J software based on the area occupied by a single cellular aggregate. C, cells were then harvested, lysed, and immunoblot analysis was performed. D, H1770 cells expressing WT DAPK3 are more sensitive to etoposide-induced apoptosis. Cells were transfected with WT or cancer mutant P216S DAPK3, maintained in low-serum growth media (0.1% FBS), and treated with vehicle or 50 μmol/L etoposide for 48 hours prior to harvesting cells and determining sub-2N DNA content by propidium iodide incorporation assays and flow cytometry. E, model depicting results of loss-of-function mutation of DAPK3 promotes increased adhesion, survival, proliferation, and drug resistance.

Figure 5.

LOF mutation in DAPK3 promotes cell–cell adhesion and chemotherapeutic resistance. A, reconstitution of DAPK3 pathway in H1770 cells that harbor a P216S mutation in DAPK3 reduces cell–cell adhesion. H1770 cells were transfected with the indicated constructs for 48 hours under high-serum conditions and cells were photographed. White bar, 0.2 mm. B, quantification of cellular aggregates is displayed and was determined by using Image J software based on the area occupied by a single cellular aggregate. C, cells were then harvested, lysed, and immunoblot analysis was performed. D, H1770 cells expressing WT DAPK3 are more sensitive to etoposide-induced apoptosis. Cells were transfected with WT or cancer mutant P216S DAPK3, maintained in low-serum growth media (0.1% FBS), and treated with vehicle or 50 μmol/L etoposide for 48 hours prior to harvesting cells and determining sub-2N DNA content by propidium iodide incorporation assays and flow cytometry. E, model depicting results of loss-of-function mutation of DAPK3 promotes increased adhesion, survival, proliferation, and drug resistance.

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DAPK3 is a member of the death-associated protein kinase family and regulates cellular processes, including apoptosis, adherence, and cell cycle progression/proliferation (21). Several substrates have been reported for DAPK3, including MLC2, histone H3, and Par4 (9), and these substrates are likely to be important for DAPK3-mediated regulation of cell survival and cell cycle progression. DAPK3 has been suggested to be a tumor suppressor (21), and our results strongly implicate DAPK3 as a tumor suppressing kinase. In vitro and in vivo kinetic assays reveal that mutations in DAPK3 abolish or significantly reduce the enzymatic activity of DAPK3. Furthermore, we show that the DAPK3 cancer mutants are unable to inhibit cellular processes, including cell proliferation, showing that these are loss-of-function mutations and suggesting that acquisition of these mutations by cancer cells will confer selective proliferative or survival advantages.

Previous reports have given us mechanistic insight into DAPK3 regulation and provide evidence that inactive DAPK3 forms a symmetrical homodimer through activation loop strand exchange between subunits (15, 18). The kinase domain of DAPK3 is critical for the homodimerzation and alone is sufficient to interact with full-length DAPK3 (18). We present data showing that inactive DAPK3 proteins expressed from a mutant allele could act in a dominant-negative fashion by binding to WT DAPK3 and preventing its activation by T180 transphosphorylation (15). The data presented provide a mechanism, in which mutations observed in cancer may not only abolish the activity of the mutant allele, but also act to suppress the function of the WT allele.

To determine whether DAPK3 loss-of-function mutations confer an advantage in the context of a tumor where the mutation was acquired, we sought to reconstitute the H1770 NSCLC cell line with WT DAPK3 and monitor phenotypic effects. Interestingly, expression of DAPK3 in the H1770 cell line resulted in a decrease in cellular aggregation, consistent with a loss of cell-to-cell adhesion that is likely to occur following MLC2 phosphorylation. Interestingly, other MLC2 kinases are also mutated in this cell line [MYLK2 (A117V) and CDC42BPA (MRCK, E50K)] and the functional consequences of these mutations have not been determined. If these mutations were loss-of-function mutations, it would suggest a decrease in MLC2 phosphorylation (at both Ser19 and Thr18) was a critical step in the evolution of the tumor and potentially explains why increasing MLC2 phosphorylation at T18/S19 would have dramatic phenotypic consequences. Additionally, this observation may provide a novel insight into a pathway that could drive increased proliferation and drug resistance and could be a common pathway utilized to promote tumorigenesis. Cell-to-cell adhesion has previously been shown to promote drug resistance, and, consistent with this observation, H1770 cells expressing WT DAPK3, which promotes loss of cell-to-cell adhesion, are more sensitive to chemotherapy (22). However, the role MLC2 phosphorylation in tumor formation is likely to be much more complex, and indeed activating mutations in ROCK1 have been described and increased MLC2 phosphorylation is also likely to play a critical role in tumor progression and increased migration (23). Interestingly, one ROCK1-activating mutation described in the study by Lochhead and colleagues, P1193S, which lies in the PH domain, was shown to be an activating mutation (23), and this mutation is also present in the H1770 cell line. The previous report did not examine MLC2 phosphorylation in H1770 cells, and our data suggest that overall MLC2 phosphorylation in these cells is barely detectable. It could be that this mutation promotes an increase in phosphorylation of another ROCK1-specific substrate or a small pool of MLC2. Comparing the various ROCK1 mutations evaluated in this study indicates that the P1193S did not appear to increase MLC2 phosphorylation significantly above WT ROCK1 and much less than the other 2 activating mutations. Additionally, P1193S ROCK1 appeared to have a unique cellular localization, and this could be because of a structural change in the PH domain induced by the mutation. Different consequences of MLC2 phosphorylation at S19 and combined phosphorylation at T18/S19 may contribute to the complex results.

Consistent with our general hypothesis, recent revelations have shown that decreased MLC2 phosphorylation may promote tumorigenesis (24). Specifically, loss-of-function mutations in LKB1 should lead to decreased NUAK1 activation, resulting in decreased inhibition of the MLC2 phosphatase, MYPT1, due to decreased 14-3-3 binding (24). MYPT1 would then be free to dephosphorylate MLC2 and maintain it in a dephosphorylated state (24). In agreement with this hypothesis, MCF-10A cells depleted of LKB1 grow as abnormally large acini with multilayering of acinar cells (25). Loss-of-function mutations in DAPK3 would be another mechanism to promote decreased MLC2 phosphorylation. Interestingly, the H157 NSCLC cell line has a loss-of-function mutation in LKB1, and this cell line is very responsive to increases in MLC2 phosphorylation, likely because phosphorylation of the substrate is normally suppressed by increased MYPT1 activity (26). Thus, loss-of-function mutations in either LKB1 or DAPK3 could lead to suppression of MLC2 phosphorylation, promote cell-to-cell adhesion and lead to increased drug resistance (Fig. 5D). Drugs that could promote MLC2 phosphorylation (or possibly diphosphorylation at T18/S19) could be valuable in the clinic in combination with other chemotherapeutics, particularly in patients with DAPK3 or LKB1 mutations, or decreased DAPK3 expression, such as in gastric cancer, where a recent study showed that 111 of 162 gastric carcinomas displayed decreased expression of DAPK3, which was associated with increased metastasis and invasion (27).

Summary

Cancer genomic studies that identify somatic mutations acquired in cancer cells will provide a roadmap for the identification and characterization of new oncogenes and tumor suppressors (28). These screens uncover mutations in known tumor suppressing kinases, such as LKB1 and ATM, verifying the ability of these screens to identify mutationally inactivated tumor suppressors (8). Functional analysis of the consequences of cancer-associated mutations in signaling proteins, such as protein kinases, will lead to a broader understanding of signaling networks that contribute to tumorigenesis and define new targets that regulate these crucial networks. Combining the genomic screens with evolving and improving bioinformatics screens will provide a platform for basic researchers to identify new critical regulators of tumorigenesis that have a lower frequency of mutations, yet are still essential for tumorigenesis. The results presented illustrate the potential of this approach and suggest that DAPK3 is a tumor suppressor with loss-of-function mutations in cancer patients.

No potential conflicts of interest were disclosed.

This work was supported by Cancer Research UK (J. Brognard), American Cancer Society Postdoctoral Grant (#116653; J. Brognard), NCI Training Grant (#T32 CA009523; J. Brognard), US Public Health Service Grants CA14195 and CA82683 from the NCI (T. Hunter), and a sanofi-aventis Discovery grant (T. Hunter). T. Hunter is a Frank and Else Schilling American Cancer Society Professor.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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