CHK1 as a therapeutic target to bypass chemoresistance in AML

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Science Signaling  13 Sep 2016:
Vol. 9, Issue 445, pp. ra90
DOI: 10.1126/scisignal.aac9704

New hope for AML patients

A pair of papers provides new hope for patients with acute myeloid leukemia (AML) by showing that the DNA replication checkpoint pathway is a viable target for therapeutic intervention. By integrating survival data from 198 treated AML patients with gene expression data for genes encoding proteins involved in the regulation of DNA replication, David et al. identified the CHEK1 gene and its product, the DNA replication checkpoint kinase CHK1, as both a prognostic indicator of survival and a therapeutic target to overcome resistance to the current standard of chemotherapy. The patients had all received standard-of-care chemotherapy. Patients with high expression of CHEK1 in their AML cells had reduced survival, and AML patient cells with high CHK1 abundance were resistant to the toxic effects of the DNA replication inhibitor cytarabine. CHK1 is activated by the kinase ATR in response to DNA replication stress arising from DNA damage. The identification of CHEK1 expression as high in lymphomas and leukemias, including AML, prompted Morgado-Palacin et al. to investigate targeting ATR and ATM, the most upstream kinases in the DNA damage response, as possible AML therapies. AML cells with oncogenic rearrangements in MLL are particularly resistant to genotoxic therapies that form the backbone of AML treatment. Inhibiting ATR resulted in death of AMLMLL cells in culture and exhibited antitumoral activity in AMLMLL mouse models. Inhibiting ATM also prolonged survival of the allograft mouse model, indicating that targeting the DNA damage response pathways alone or in combination with other chemotherapeutic agents may be beneficial in patients with AML.


The nucleoside analog cytarabine, an inhibitor of DNA replication fork progression that results in DNA damage, is currently used in the treatment of acute myeloid leukemia (AML). We explored the prognostic value of the expression of 72 genes involved in various aspects of DNA replication in a set of 198 AML patients treated by cytarabine-based chemotherapy. We unveiled that high expression of the DNA replication checkpoint gene CHEK1 is a prognostic marker associated with shorter overall, event-free, and relapse-free survivals and determined that the expression of CHEK1 can predict more frequent and earlier postremission relapse. CHEK1 encodes checkpoint kinase 1 (CHK1), which is activated by the kinase ATR when DNA replication is impaired by DNA damage. High abundance of CHK1 in AML patient cells correlated with higher clonogenic ability and more efficient DNA replication fork progression upon cytarabine treatment. Exposing the patient cells with the high abundance of CHK1 to SCH900776, an inhibitor of the kinase activity of CHK1, reduced clonogenic ability and progression of DNA replication in the presence of cytarabine. These results indicated that some AML cells rely on an efficient CHK1-mediated replication stress response for viability and that therapeutic strategies that inhibit CHK1 could extend current cytarabine-based treatments and overcome drug resistance. Furthermore, monitoring CHEK1 expression could be used both as a predictor of outcome and as a marker to select AML patients for CHK1 inhibitor treatments.


Acute myeloid leukemia (AML) is a heterogeneous disease characterized by different recurrent cytogenetic and molecular aberrations that occur in hematopoietic progenitor cells and alter the growth, differentiation, and proliferation capacities of the progenitor cells. The treatment of AML has remained a huge challenge for oncohematologists. Although roughly 70% of adults with AML under age 60 achieve a complete remission with traditional cytarabine- and anthracycline-based induction regimens, the overall long-term survival (>5 years) rate with therapy is only 30 to 40% (1, 2). The prognosis is even worse for older patients, for which long-term survival is less than 10% (3). Whereas recurrent chromosomal structural alterations are well-established prognostic markers, the outcome of patients with a normal karyotype, referred to as cytogenetically normal AML (CN-AML), has been recently better defined with the discoveries of recurrent mutations in the FLT3, NPM1, and CEBPA genes. These genetic alterations have been associated with favorable or negative outcome, depending on the mutated gene, after standard intensive chemotherapy. Consequently, the presence of these mutations now serves as a basis for molecularly guided risk assessment and treatment stratification (4). In addition, altered expression of genes such as MN1, BAALC, and ERG is also predictive for outcomes of patients with CN-AML (57). However, the roles of most of these genes in leukemogenesis remain unclear, and many of their products are not optimal drug targets.

Here, we reasoned that DNA replication represents a less explored source of prognostic markers that could be used in combination with cytogenetics to predict AML prognosis and eventually provide potential targets for therapeutic targeting. Accurate execution of the DNA replication program limits cancer risk by preserving genome integrity. Multiple studies of solid cancers have provided evidence that defective or dysregulated DNA replication program triggers replicative stress, leading to the accumulation of genetic alterations (812). However, defective DNA replication as a source of markers in hematological malignancies has not been explored. Genome-wide analyses detected widespread dysregulation of replication timing in different types of leukemic cells (13). Because the defect in replication timing occurred at the same temporal aspect of the replication process, leukemogenesis may be associated with a common early replication defect event (13). Here, we test the hypothesis that misexpression of genes encoding proteins involved in DNA replication occurs in AML and contributes to the cytogenetic aberrations that characterize AML. We speculated that specific signatures of genes encoding proteins involved in the regulation of DNA replication could be relevant during relapses and thus represent predictors of outcome for AML patients. We also reasoned that a modified DNA replication program could affect the response to current standard AML treatment, the nucleoside analog cytarabine, an inhibitor of DNA chain elongation during replication fork progression.

Using information and cells from 198 treated AML patients, we examined the prognostic significance of the expression of genes encoding proteins involved in DNA replication as a determinant of survival, and tested the encoded proteins as possible therapeutic targets in AML. Our data demonstrate that CHEK1 expression alone is a prognostic marker in AML and that high abundance of the encoded protein checkpoint kinase 1 (CHK1) protects AML cells against the toxic effects of the therapeutic agent cytarabine.


Established prognostic markers validate the AML cohort

The clinical characteristics of the AML cohort used in this study are described in Materials and Methods and shown in table S1. Because high BAALC, ERG, or MN1 expression was previously reported as an indicative of poor prognosis in CN-AML patients in several independent studies (57), we first confirmed that increased abundance of the expression of these three genes, measured by the Fluidigm system, was significantly associated with a reduction in the overall survival in our cohort (table S2). Overall survival was calculated from the date of the first day of chemotherapy until the date of death from any cause. For the 90 CN-AML patients, high expression (greater than the median expression value) of BAALC, ERG, or MN1 was associated with an increase in death rate with respective hazard ratios (HRs), an indicator of the death rate between low versus high expression, of 2.11, 2.3, and 1.63 (fig. S1 and table S2). Collectively, these data validate the cohort chosen for the study as representative of CN-AML patients.

High CHEK1 expression is an independent prognostic marker in AML

We then monitored the expression of a subset of genes encoding proteins involved in different aspects of DNA replication, including those that maintain the stability of stalled DNA replication forks and those that control the DNA replication checkpoint (table S3). Among all the genes tested, the replication checkpoint gene CHEK1 was the strongest factor associated with outcome (overall, event-free, and relapse-free survival) (Fig. 1). Thus, we divided the patient data into two groups according to their CHEK1 median expression: low or high expressers (Table 1). The complete response, measured as the full recovery of blood counts, did not differ according to CHEK1 expression status, with 83 of 99 (83.8%) and 79 of 99 (79.8%) achieving a complete response in both the CHEK1high and CHEK1low groups, respectively (P = 0.46). There were also no significant differences between patients with low and high CHEK1 expression in age, sex, white blood cell count, cytogenetics, or FLT3-ITD and NPM1 status (Table 1). Consequently, we focused on the relationship between CHEK1 expression and the survival and relapse rate predictions.

Table 1 Characteristics of 198 patients in the study population.
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Fig. 1 Relationship between survival and CHEK1 expression.

(A to C) Overall survival (A), event-free survival (B), and relapse-free survival (C) according to CHEK1 expression. Dashed lines, high CHEK1 expression patients (ΔΔCt CHEK1 > median); solid lines, low CHEK1 expression patients (ΔΔCt CHEK1 < median).

First, we found that CHEK1 expression predicted overall survival with a 5-year estimate of 29.5% in the CHEK1high group and 52.4% in the CHEK1low group (Fig. 1A and Table 2). We calculated event-free survival from the first day of chemotherapy until the date of treatment failure, relapse, or patient death from any cause. High CHEK1 expression predicted shorter event-free survival, with 5-year estimates of 24.2% for the CHEK1high group and 50.3% for the CHEK1low group (Fig. 1B and Table 2). Relapse-free survival for patients who achieved complete response was calculated from the date of complete response until the date of relapse or death from any cause. For the 162 patients with complete response (Table 1), 5-year relapse-free survival was significantly lower in the CHEK1high group (30.4%) than in the CHEK1low group (60%) (Fig. 1C and Table 2).

Table 2 Survival estimations using Kaplan-Meier method and comparisons between high and low CHEK1 expression.
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Table 3 Multivariate survival analysis by Cox proportional hazard models.
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Consistent with the relapse-free survival data, CHEK1 expression was also indicative of relapse rates. Five-year cumulative incidence of relapse, measured from the date of complete response until the date of relapse, was significantly different between both groups (Fig. 2, left). CHEK1 expression did not correlate with patients who deceased without relapse where death was independent of AML (Fig. 2, right). Similarly, cumulative cause-specific hazards of relapse were also significantly different between both groups (fig. S2). There was no difference in the rate of death without relapse between low and high CHEK1 groups (fig. S2).

Fig. 2 Relationship between relapse and death and CHEK1 expression.

Cumulative incidence of relapse (left) and death (right) according to CHEK1 expression. Dashed lines, high CHEK1 expression patients (ΔΔCt CHEK1 > median); solid lines, low CHEK1 expression patients (ΔΔCt CHEK1 < median).

We also analyzed the cohort by subpopulations of patients who had received allogeneic stem cell transplants (allografted patients) (Fig. 3, A and B and fig. S3) and patients with the MLL translocation (Fig. 3C), a genetic abnormality that confers particularly poor prognosis (14). We also found that CHEK1 expression was significantly associated with overall survival in allografted patients and high CHEK1 expression was associated with reduced relapse-free survival (Fig. 3 and fig. S3). The patients with the MLL translocation already have a poor prognosis, and with only 12 patients in the cohort, survival analysis would not be informative. However, the average CHEK1 expression was 3.6 times higher in patients with MLL translocation than in the rest of 171 cases in the cohort (Fig. 3C).

Fig. 3 A study of CHEK1 expression in subpopulations of allografted patients and patients with the MLL translocation.

(A) Overall survival according to CHEK1 expression in patients who were allografted in first complete response (n = 50). (B) Relapse-free survival according to CHEK1 expression in patients who were allografted in first complete response (n = 50). (C) CHEK1 expression analysis in patients with MLL translocation. Twelve cases in the cohort contained the MLL translocation (MLL+), and 171 cases in the cohort did not (MLL).

The multivariate analyses based on Cox proportional hazard models adjusted for age, sex, year of diagnosis, and white blood cell count revealed that the value of CHEK1 expression is independent of these factors (Tables 3 and 4). Furthermore, CHEK1 expression did not significantly affect overall survival in the favorable-risk groups, including 29 patients with NPM1 mutation without FLT3 mutation (Tables 1 and 4).

Table 4 Estimated cause-specific and subdistribution HRs for relapse and death of other cause using unadjusted and adjusted regression models.
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High CHK1 abundance in AML patients does not correlate with higher activation of the DNA replication checkpoint

We quantified CHK1 abundance in 10 AML samples from patients in the cohort tested in the transcript Fluidigm study (see Fig. 1). CHEK1 transcript abundance and CHK1 protein abundance are correlated (Fig. 4, A and B). Analysis of samples from an additional 37 AML patients by immunoblot and immunofluorescence revealed that the samples could be divided into 21 low CHK1 abundance samples (exemplified by #36) and 16 high CHK1 abundance samples (exemplified by #37) [Fig. 4, C and D (lower panel), and table S4]. The analysis of high CHK1 abundance patient samples by immunofluorescence revealed a subpopulation of cells within the samples with a much higher amount of CHK1 [Fig. 4, C and D (upper panel)].

Fig. 4 Identification of high and low CHK1 abundance in patient cells.

(A and B) Correlation between CHEK1 mRNA and CHK1 protein abundance in AML patient samples. (A) CHK1 abundance was determined by immunoblot using the antibodies against CHK1 and actin as a loading control in 10 AML patient samples. Extracts from KG1a leukemic cell line served as a between-gel control. (B) Linear regression analysis for the correlation between CHEK1 mRNA level and CHK1 protein level. Statistical analysis was performed using Spearman’s test. (C and D) Quantification of CHK1 in two representative samples of the 37 AML samples of the cohort. (C) Representative images by immunofluorescence staining of samples #36 and #37. (D) Upper panel: Quantification of CHK1 in samples #36 and #37. Each solid circle or square corresponds to one cell; at least 100 cells were scored for each condition. Horizontal lines, mean values. Statistical analyses were performed using unpaired t test with Welch’s correction (****P < 0.0001). (D) Lower panel: Western blot analyses of CHK1 abundance in representative AML samples [#36, #37, and control (CTL)]. Actin served as a loading control. A.U., arbitrary units. (E) Western blot analysis of CHK1 abundance, CHK1 phosphorylation (p-CHK1), and CDC25A abundance in AML patient cells.

The ATR replication checkpoint pathway becomes activated when DNA replication is stalled. This ATR checkpoint involves the translocation of multicomponent protein complexes to the stalled replication forks, which is then followed by the phosphorylation of the ATR effector CHK1 (15). Therefore, we explored whether high CHK1 abundance in AML patient cells isolated from the patients at diagnosis correlated with the amount of phosphorylated and activated CHK1, indicating activation of the DNA replication checkpoint before exposure to the drug. We assessed the abundance of CHK1, the phosphorylation of CHK1 on Ser345, and the abundance of CDC25A, a CHK1 substrate that becomes targeted for degradation upon phosphorylation by CHK1 (16), in cell extracts from a set of eight randomly chosen samples from the initial cohort. CHK1 abundance did not correlate with CHK1 phosphorylation status or CDC25A abundance (Fig. 4E), suggesting that CHK1 abundance was not indicative of activation of the DNA replication checkpoint. Thus, although high CHEK1 expression, and by extension CHK1 abundance (Fig. 4B), correlated with poor patient survival and relapse, these data indicated that this was not due to chronic activation of the DNA replication checkpoint.

Resistance to cytarabine in AML samples correlates with increased abundance of CHK1 and is abolished by the CHK1 inhibitor SCH900776

We assessed whether AML primary cells with low and high CHK1 abundance had different sensitivity to the toxic effects of cytarabine. We quantified the ability of the cells to form colonies in methylcellulose-based semisolid medium when exposed to clinically relevant concentrations (5 and 10 nM) of cytarabine (Fig. 5A). In the colony-forming assay, cells with high CHK1 abundance were significantly more resistant to cytarabine compared to the low CHK1 abundance cells (Fig. 5A). Because high CHK1 abundance correlated with higher AML cell survival in the presence of cytarabine, we also tested whether inhibition of the kinase activity of CHK1 restored the sensitivity of these leukemic cells to the toxic effects of cytarabine. Addition of the CHK1 inhibitor (SCH900776) increased cytarabine sensitivity of the high CHK1 abundance cells such that they exhibited a similar sensitivity to inhibition of colony formation as the low CHK1 abundance cells (Fig. 5A). These data support a direct link between the increased abundance of CHK1 and resistance to cytarabine in AML cells.

Fig. 5 Increased CHK1 abundance favors AML cell proliferation and efficient DNA replication fork progression upon arabinosylcytosine treatment.

(A) Analyses of the clonogenic properties of high CHK1 abundance (high CHK1) and low CHK1 abundance (low CHK1) AML blast cells upon continuous exposure to 5 or 10 nM arabinosylcytosine (Ara-C) alone or in combination with 250 nM CHK1 inhibitor (SCH900776). Colony formation was assessed after 7 days and represented as the ratio of the number of clones scored between untreated and treated conditions. Horizontal lines correspond to mean value (n = 22 for low CHK1 abundance cells and n = 15 for high CHK1 abundance cells). Statistical analyses were performed using the Mann-Whitney test (5 and 10 nM Ara-C, *P = 0.0242 and P = 0.0173, respectively; 10 nM Ara-C and 250 nM SCH900776, *P = 0.0398). CFU-L, leukemic colony-forming units. (B to E) Analysis of the DNA replication fork progression by DNA spreading. (B and D) Representative fibers from low and high abundance CHK1 AML cells collected after 7 days in clonogenic assays in the presence of Ara-C (5 nM), without Ara-C, or in the presence of Ara-C (5 nM) and SCH900776 (250 nM). (C and E) Quantitative analysis of iododeoxyuridine (IdU) track length under different treatment conditions. Thirty fibers per samples were analyzed (n = 2 for low CHK1 abundance cells and n = 2 for high CHK1 abundance cells). Statistical analysis was performed using unpaired t test with Welch’s correction (**P = 0.0018, ****P < 0.0001). N.S., not significant.

Because we found no correlation between CHK1 abundance and the activation of its function for replication checkpoint in AML patients, we predicted that the mechanism of resistance to cytarabine is independent of the role of CHK1 in the ATR replication checkpoint pathway. Instead, we hypothesized that the high abundance of CHK1 ensured the maintenance of active replication forks when the patient received cytarabine. Without abundant CHK1, cytarabine terminates DNA polymerization during progression of the replication forks, thereby causing stalled replication forks and triggering the DNA replication checkpoint. To test whether high CHK1 abundance enabled cells to bypass cytarabine-induced stalling of the replication forks, we analyzed the DNA replication forks in cellulo both at the whole-genome and at the single-molecule levels with the DNA fiber spreading technique, which labels tracks of new DNA synthesis in vivo (17). With this technique, we monitored replication fork progression as indicated by the presence of nascent DNA at the level of individual replicating DNA molecules. With this method, a reduction in nascent DNA track length is indicative of stalled DNA replication. Cytarabine induced a significant reduction in nascent DNA track length in low CHK1 abundance cells (Fig. 5, B and C). In contrast, similar treatment with cytarabine had no significant impact on DNA replication fork progression in high CHK1 abundance cells (Fig. 5, D and E). However, addition of the CHK1 inhibitor SCH900776 to the cytarabine-treated, high CHK1 abundance cells triggered a significant reduction in nascent DNA track length (Fig. 5, D and E). Collectively, these data showed that high CHK1 abundance and a CHK1 activity inhibited by SCH900776 facilitate the progression or restart of cytarabine-induced stalled DNA replication forks.

Increased abundance of CHK1 in AML cells enhances clonogenic ability

One scenario that may reconcile the resistant phenotype and the enhanced potential for aggressive relapse of cells with abundant CHK1 is that a preexisting subpopulation of high CHK1 abundance AML cells (Fig. 4, C and D), which can be selected and enriched by the treatment with cytarabine, has the potential to form a residual aggressive tumor burden after treatment. Therefore, we used the colony-forming assay to compare the aggressiveness potential of AML cells with low or high CHK1 abundance in the absence of cytarabine. Consistent with the hypothesis, we found that high abundance of CHK1 increased not only the number of colonies that formed (Fig. 6A) but also the number of cells in each clonal colony (Fig. 6, B and C), in comparison to the AML cells with low CHK1 abundance.

Fig. 6 Increased abundance of CHK1 favors leukemic clonogenicity.

(A) The clonogenic properties were analyzed by scoring AML cell colonies from cells with high and low CHK1 abundance at day 7. Results are expressed as absolute number of colonies. Horizontal lines, mean value (n = 22 for low CHK1 abundance cells and n = 15 for high CHK1 abundance cells). Statistical analysis was performed using the Mann-Whitney test (*P = 0.0371). (B and C) The histograms indicate the distribution of AML samples from low CHK1 abundance cells and high CHK1 abundance cells characterized for their ability to form small-, medium-, and large-sized clones after 7 days. Small-sized clones contained less than 9 cells, medium-sized clones contained between 9 and 20 cells, and large-sized clones contained more than 20 cells. Five patients were analyzed for the low CHK1 group, and 10 patients were analyzed for the high CHK1 group. Representative images of the clones of different sizes are shown in (C). (D to F) Analysis of CDK1 abundance in low and high CHK1 abundance cells. Immunoblotting of CDK1 is shown in (D), and quantitative analyses of CDK1 mRNA transcript and CDK1 protein are shown in (E) and (F), respectively. Horizontal lines, mean value. Statistical analysis was performed using unpaired t test with Welch’s correction (*P = 0.0344, n = 18; **P = 0.0023, n = 31).

Because the cells with high CHK1 abundance had increased proliferative potential, we examined the abundance of cyclin-dependent kinase 1 (CDK1), a conserved serine/threonine kinase necessary for cell cycle progression (18). Furthermore, in absence of replication stress or DNA damage, CHK1 phosphorylates histone H3 at Thr11, which enhances the expression of CDK1 and thereby enables cells to progress into mitosis (19). In response to the activation of DNA damage or DNA replication stress, CHK1 dissociates from chromatin, which is concomitant with reduced histone H3 Thr11 phosphorylation and repression of CDK1 expression, resulting in the activation of the G2/M checkpoint. CDK1 was increased (at both the mRNA and protein levels) in unstressed AML cells with high CHK1 abundance compared to AML cells with low CHK1 abundance (Fig. 6D). These results provide a mechanism for the higher colony-forming ability of AML cells with higher CHK1 abundance.


Our data show that CHEK1 transcript abundance is an independent prognostic marker in AML. High CHEK1 transcripts in leukemic cells were associated with an increased risk of relapse and poor survival in a cohort of AML patients who had received first-line cytarabine and anthracycline chemotherapy. Several mechanisms of enhanced CHEK1 expression in cancers have been described, including gene amplifications (20) and overexpression of the gene encoding the transcriptional factor E4F1, described as a positive regulator of CHEK1 expression (21, 22). Future studies can evaluate the importance and contribution of these mechanisms in high CHEK1–expressing AML cells. Because we found that in primary cells from nine AML patients CHEK1 transcripts correlated with CHK1 protein abundance, either transcript analysis or protein analysis by flow cytometry with suitable antibodies could be used to monitor CHK1 abundance in AML patient samples. The identification of relevant molecular markers is of great importance in AML because current molecular classification patterns do not fully predict the heterogeneity in outcome of these patients. To date, only high levels of BAALC, ERG, or MN1 expression were reported to negatively affect the outcome of AML patients (57), which we confirmed here. The mechanisms through which increased expression of these genes contributes to malignant transformation remain to be elucidated. Moreover, presently, the products of these genes are not druggable targets.

Our analyses also revealed that high CHK1 protein abundance does not correlate with higher endogenous activation of the replication checkpoint yet favored AML cell survival and proliferation of cells exposed to clinically relevant concentrations of cytarabine. This finding could explain why AML patients classified as high-expressing CHEK1 by Fluidigm assay and treated by intensive cytarabine-based chemotherapy have a poor clinical outcome. To explain why these patients also showed a higher relapse rate months or years after the treatment, we propose that a preexisting subpopulation of AML cells with increased abundance of CHK1 survives the selective pressure of cytarabine during the treatment, becoming enriched and forming a residual aggressive tumor burden that is the source of the relapse. This hypothesis is supported by our findings that (i) AML cells with increased average quantity of CHK1 are heterogeneous for CHK1 abundance with a minor population having very high amounts of CHK1 (Fig. 4, C and D), (ii) AML cells with increased average abundance of CHK1 were associated with increased abundance of the cell cycle–promoting protein CDK1 and displayed a higher ability to form cellular clones in methylcellulose in the absence of any treatment (Fig. 6), and (iii) the high CHEK1 group of AML patients showed a striking relapse rate also after allogeneic stem cell transplantation (Fig. 3 and fig. S3).

CHEK1 encodes the transducer and effector protein kinase CHK1 that regulates cell cycle progression and chromosome metabolism in response to genotoxic stress (23), particularly during S phase during which CHK1 has been proposed to maintain the stability of stalled DNA replication forks in response to replication inhibitors (23, 24). With a single-molecule DNA fiber spreading technique, we showed that inhibition of fork progression by cytarabine was significantly more pronounced in AML cells with low abundance of CHK1 compared to those with high CHK1 abundance, supporting the hypothesis that high CHK1 abundance in AML cells could facilitate replication fork progression or restart stalled forks upon cytarabine treatment.

The control of fork elongation by CHK1 could be independent of its function in the regulation of the firing of replication origins (24). Because cytarabine acts as a DNA chain elongation inhibitor in the course of the DNA replication fork progression, the treatment with this nucleotide analog leads to the accumulation of stalled forks, which in turn favors fork collapse and chromosomal breakage, events that are detrimental for cell survival. Just as the role of CHK1 in cells experiencing DNA replication stress is to protect cells (25), the high abundance of CHK1 could protect AML cells from cytarabine toxicity by enhancing stabilization of stalled forks, avoiding fork collapse, and enabling fork restart. Consistent with this mechanism of CHK1 action in AML, supraphysiological abundance of CHK1 protects cells from endogenous replication stress in mice (25) and in primary T cell acute lymphoblastic leukemia cells (26). Our results support the notion that a modified DNA replication program in AML, through high abundance of CHK1, could be a mechanism by which the cells adapt to DNA replication stress, such as the accumulation of stalled forks induced by cytarabine. The restart of these stabilized forks could be mutagenic. Cytarabine induces an increased mutation rate in relapsed AML patients (27). Together with recent findings that have demonstrated abnormal temporal control of DNA replication in several hematologic malignancies (13), our results also suggest that these changes in the replication program enable cell survival despite endogenous replication stress and, in AML, contribute to chromosomal instability and the aggressive progression of the pathology.

Finally, we provide evidence that a CHK1 inhibitor enhanced the inhibition of colony formation and fork progression by cytarabine in primary AML with high CHK1 abundance. The CHK1 inhibitor SCH900776 sensitized high CHK1 abundance group to cytarabine to the same level as that of the low CHK1 abundance group treated with cytarabine alone. These observations have potential important implications in view of current efforts to enhance the efficacy of cytarabine-containing AML regimens, especially for refractory patients. They will also give important keys for analyzing the results of the phase 2 clinical trial that is in progress for SCH900776 in AML (;NCT01870596) (28). Compared with the rest of the patient cohort, the average CHEK1 expression was 3.6 times higher in patients with the MLL translocation, which is known to confer poor prognosis. Therefore, targeting CHK1 could be a strategy for treating AML with MLL translocation, especially because these tumors have an inherent deficiency in responding to DNA damage. Morgado-Palacin et al. (29) provide compelling preclinical analysis of this therapeutic strategy.

In conclusion, we propose that high levels of CHK1 protein favor not only proliferation of leukemic cells but also cell viability upon intensive chemotherapy through an efficient DNA replication stress response and that novel therapeutic strategies that aim at inhibiting CHK1 could extend our current cytarabine-based approaches, overcome active drug resistance pathways, and eventually improve the outcome of patients with AML. Thus, monitoring expression of this key gene could be used both as a strong predictor of outcome and as a marker to select AML patients for treatment with CHK1 inhibitors.



Between 1 January 2000 and 31 December 2010, 513 consecutive patients (65 years of age or younger) with a new diagnosis of AML have been treated by intensive chemotherapy in our center. Diagnosis workup and treatment modalities have been described elsewhere (30, 31). The cytogenetic risk was established according to the Medical Research Council classification (32). The use of fresh and thawed samples (or derivative products, such as DNA and RNA) from 198 AML patients as well the analysis of CHK1 abundance from these samples have been performed after informed consent and stored at the HIMIP collection (BB-0033-00060). The characteristics and outcomes of the remaining 315 nontested patients as compared to the 198 tested patients are shown in table S1. According to the French law, HIMIP collections have been declared to the Ministry of Higher Education and Research (DC 2008-307 collection 1), and we obtained a transfer agreement (AC 2008-129) after approbation by the Comité de Protection des Personnes Sud-Ouest et Outre-Mer II (ethical committee). Clinical and biological annotations of the samples have been declared to the CNIL (Comité National Informatique et Libertés, that is, Data processing and Liberties National Committee), and these are supported by CAPTOR (Cancer Pharmacology of Toulouse-Oncopole and Region). This study was approved by the institutional review board (Ethical Committee of Research).

Total RNA extraction and reverse transcription PCR

Total RNA was extracted from frozen cells (7 million to 15 million of cells) stored in 1 ml of TRI Reagent RNA/DNA/protein isolation reagent (Molecular Research Center). The extraction was done by adding 200 μl of cold Ready-Red chloroform-isoamyl alcohol (MP Biomedicals) and vigorously shaking for 15 s using a vortex, and then the sample was incubated on ice for at least 5 min. After centrifugation at 13,000 rpm in a microfuge for 15 min at 4°C, the upper aqueous phase was transferred into a new vial. One volume of isopropanol was added, and the sample was vortexed and incubated for 1 hour at −20°C. After centrifugation at 13,000 rpm in a microfuge for 15 min at 4°C, the pellet was dried and 1 ml of cold 75% ethanol was added. After centrifugation at 8000 rpm in a microfuge for 10 min at 4°C, the pellet was dried and incubated for 4 min at 65°C. The pellet was resuspended in 30 μl of ribonuclease (RNase)–free water with RiboLock RNase Inhibitor (40 U; Fermentas). RNA concentration was determined using the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies Inc., Thermo Fisher Scientific). RNA quality and purity were assessed on the Agilent 2100 BioAnalyzer by using the Agilent RNA 6000 Nano Kit (Agilent Technologies). Only RNAs presenting an RNA integrity number (RIN) of >6.5 were selected for expression analysis (~90% of the samples had a RIN of >8). Complementary DNA (cDNA) was generated from 1 μg of RNA with the SuperScript VILO cDNA Synthesis Kit (Invitrogen) for reverse transcription polymerase chain reaction (PCR) following the manufacturer’s suggestions. To ensure a good quality reverse transcription step, one part of each cDNA was used to check ABL1 (TaqMan Gene Expression Assay, Applied Biosystems; Hs01104728_m1) expression using ABI Prism 7300 HT (Applied Biosystems).

Specific target amplification and quantitative PCR

The other part of each cDNA was diluted in water (5 ng/μl) and used for target amplification by BioMark Dynamic Arrays (Fluidigm). Inventoried TaqMan assays (Applied Biosystems) were pooled using 84 probes and primer pairs (72 DNA replication genes, 9 housekeeping genes, and BAALC, ERG, and MN1; the latter 3 are genes reported to negatively affect the outcome of CN-AML patients) (table S2) to a final concentration of 0.2× for each of the 84 assays. For CHEK1, the assay ID was Hs00967506_m1 and the reference of the DNA sequence was NM_001114121.2, localized at exons 6 and 7. To increase sensitivity, a multiplexed preamplification process was performed for the pool on every 1.25 μl of cDNA using 14 cycles of cDNA preamplification step (at 95°C for 15 s and at 60°C 4 min) and TaqMan PreAmp Master Mix (Applied Biosystems) in a standard PCR thermocycler. Preamplified cDNA was diluted 1:5 in 10 mM tris, 1 mM EDTA. Diluted cDNA (2.25 μl) was added to 2.5 μl of TaqMan Universal PCR Master Mix (Applied Biosystems) and 0.25 μl of GE Sample Loading Reagent (Fluidigm). In a separate tube, 3.5 μl of TaqMan assay was added to 3.5 μl of the GE Sample Loading Reagent. cDNA samples (5 μl) were loaded onto the sample inlet wells, and assay samples (5 μl) were loaded onto assay detector inlets. Because 198 samples were to be analyzed in duplicate, five 96.96 Dynamic Arrays (Fluidigm) were used. For each plate, one well was loaded with H2O as a control for contamination. Genomic DNA (gDNA) from three different patients was loaded to check whether TaqMan assays can also amplify gDNA. To verify specific target amplification efficiency, a sample of control gDNA and assay control RNase P TaqMan probe was treated (Life Technologies, PN 4316844), preamplified, and quantified using the same TaqMan PreAmp Master Mix (Applied Biosystems). The expected value of cycle quantitation was between 12 and 13. To perform interplate calibration, a sample calibrator made of cDNA from patient #1 was included in duplicate in each plate. The chip was primed and placed into the NanoFlex Integrated fluidic circuit controller, where 8 nl of cDNA and 1 nl of Assay were mixed. Real-time PCR analysis was completed on the BioMark System (Fluidigm).

Data processing

Raw data obtained from the system’s software, using the auto detector function to establish the threshold setting (BioMark Real-Time PCR Analysis v2.1.1, Fluidigm), were checked using the graphical representation of the plate layout. Among all reactions investigated, none were rejected due to bubbles or instable ROX (carboxy-X-rhodamine) signal. All amplification curves were displayed for each well of the calibrator sample. When the threshold for cycle did not meet quality criteria (that is, the threshold occurred in the linear phase of the amplification curve instead of the exponential phase), the threshold value was set manually. The threshold established for the first dynamic array was applied to the four other dynamic arrays. Wells with very high (>26), absent (999), or very low (<2) endogenous Ct were excluded.

Normalization method

We normalized the real-time quantitative PCR data with the data obtained with the housekeeping genes and performed the interplate calibration using the qbase+ algorithm as described (33). Among the nine housekeeping genes tested [GUSB (Hs99999908_m1), ACTB (Hs99999903_m1), ABL1 (Hs01104728_m1), G6PD (Hs00166169_m1), TBP (Hs00427621_m1), GAPDH (Hs03929097_g1), HMBS (Hs00609293_g1), B2M (Hs00984230_m1), and UBC (Hs00824723_m1)], geNorm algorithm determined the four most stable, which were GAPDH, GUSB, TBP, and ABL1, and these were used to calculate the gene expression normalization factor. Expression values are given in ΔΔCt. BAALC (Hs00227249_m1), ERG (Hs01554635_m1), and MN1 (Hs00159202_m1) genes were used to validate our cohort data.

Statistical analysis

We explored the association between the expression of 72 DNA replication genes of interest (table S3) and the different survival endpoints. Complete response required a normocellular bone marrow with >5% blasts and no Auer rods, a neutrophil count of ≥ 1 × 109/liter, and a platelet count of ≥100 × 109/liter, without evidence of extramedullary disease after one or two courses of treatment. Induction failures included deaths in aplasia and resistant disease. After applying a multiple testing correction by the Benjamini-Yekutieli method (for a global type I risk of 5%), a P value lower than 0.000136 was needed for statistical significance, and none of the 72 gene expressions was significantly associated with overall, event-free, or relapse-free survival. Because our sample size was limited to 198 in this study, HRs lower than 2.1 to 2.3 were not expected to be statistically significant with a power of 80%. As an alternative to P values for identifying the potentially most interesting genes, we performed an analysis of the dose-effect relationship between gene expression and survival, as suggested elsewhere (34). CHEK1 expression appeared to be the most significantly associated with patient survival with a stable dose-effect relationship. Consequently, we focused the analysis on CHEK1 expression only. We categorized gene expression in a binary variable “high expression” versus “low expression” from the median expression value. Clinical characteristics were compared according to the gene expression level using χ2 tests. We estimated the overall, event-free, and relapse-free survival functions using the Kaplan-Meier method. The median follow-up among patients who were still alive at the date of last contact (n = 80) was 68.3 months (range, 29.0 to 132.0). High and low CHEK1 expression groups were compared using log-rank tests. For multivariate analyses, we applied Cox proportional hazards models adjusted for sex, age, white blood cell count, year of diagnosis, and cytogenetic risk group. An internal validation procedure was computed to correct the expected overoptimism of Cox models for overall, event-free, and relapse-free survival. We applied the bootstrap cross-validation procedure described previously (35). We derived slope indices from 200 bootstrap samples and used them as shrinkage factors by multiplying slope indices with regression coefficients. Slope indices were computed using the “rms” package for R (Harrell FE [2009]: rms: S Functions for Biostatistical/Epidemiologic Modeling, Testing, Estimation, Validation, Graphics, and Prediction). We performed the competitive risk analysis following the approach recommended by Latouche et al. (36), using the Cox model (to calculate cause-specific HRs) and the Fine-Gray model (to calculate subdistribution HRs), and presenting the results for all causes side by side. All analyses were performed using Stata Statistical Software (release 11.2; Stata Corporation), except for the internal validation procedure that was achieved with R (v3.0.1) [R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2013);].

Western blot and immunofluorescence

Each sample from AML patient cells or the KG1a cell line (internal control loaded at two dilutions) was processed according to previously described Western blotting protocol (37). Briefly, before quantification, the linearity of the signal was checked for all the membranes. Then, quantification of chemiluminescent signals for CHK1 [G4 (Santa Cruz Biotechnology) against CHK1] and actin proteins for each membrane was done with the GeneTools from Syngene software (v1.4.0.0). Quantifications were performed from the ratio between CHK1 values in AML samples and the CHK1 value in the KG1a cell line, normalized by the actin values. AML patient cells are considered “high CHK1 abundance” if the average protein abundance value is 1.2-fold higher than the median.

For most of the patients (22 of 37), CHK1 protein status was confirmed by an immunofluorescence approach. Briefly, AML cells were seeded onto coverslips pretreated with 0.01% poly-l-lysine (Sigma) and processed as previously described (37, 38). CHK1 was detected with using a monoclonal antibody (C9358, Sigma). Secondary antibody labeled with Alexa 488 was purchased from Invitrogen. DNA was visualized using ProLong Gold antifade reagent with 4′,6-diamidino-2-phenylindole (Invitrogen). Images were acquired using a Zeiss Axio Observer microscope fitted with an AxioCam HRm Rev.3 camera and subsequently processed using the ImageJ or ZEN software packages.

Clonogenic assay

Primary cells from AML patients were thawed and adjusted to 1 × 105 cells/ml final concentration in H4230 methylcellulose medium (STEMCELL Technologies) supplemented with 10% 5637-CM as a stimulant (39) and Ara-C (5 and 10 nM) alone or in combination with the CHK1 inhibitor SCH900776 (250 nM). The cells were then plated in 35-mm petri dishes in duplicate and allowed to grow for 7 days in a humidified CO2 incubator (5% CO2, 37°C). At day 7, the leukemic colonies (more than five cells) were scored.

DNA fiber spreading

Exponentially growing AML cells were pulse-labeled with chlorodeoxyuridine (CldU) (20 μM) for 20 min, washed twice, and incubated with IdU (200 μM) for additional 20 min. Cells were lysed with 6 μl of 0.5% SDS, 200 mM tris-HCl (pH 7.4), and 50 mM EDTA buffer onto clean glass slides, which were tilted to allow the DNA to unwind. Samples were fixed in 3:1 methanol/acetic acid and denatured with HCl (2.5 N) for 1 hour, blocked with phosphate-buffered saline (pH 7.4) with 5% bovine serum albumin for 15 min, and incubated with a mouse antibody recognizing bromodeoxyuridine (BrdU) (Becton Dickinson) to detect IdU, a donkey Cy3-conjugated secondary antibody against mouse antibodies (Jackson ImmunoResearch), a rat antibody against BrdU (Accurate Chemicals) to detect CldU, and a donkey Alexa 488 secondary antibody (Invitrogen). Slides were mounted with Mowiol 4-88 (Calbiochem), and DNA fibers were visualized using a Zeiss Cell Observer microscope. Images were analyzed using Zeiss LSM Image Browser software and ImageJ software. Each data set is derived from measurement of 30 to 40 fibers.


Fig. S1. Kaplan-Meier survivor functions according to BAALC, ERG, or MN1 expression.

Fig. S2. Analysis of relapse and death.

Fig. S3. Overall survival, relapse-free survival, and relapse incidence according to CHEK1 expression in allografted patients.

Fig. S4. Kaplan-Meier overall survivor functions according to CHEK1 expression in the favorable-risk group.

Table S1. Clinical characteristics of the cohort.

Table S2. Overall survival according to BAALC, ERG, or MN1 expression.

Table S3. List of the genes tested for the analysis.

Table S4. Quantification method to determine CHK1 abundance in patient samples.


Acknowledgments: We gratefully acknowledge J.-J. Maoret and F. Martin for their technical contributions in the real-time PCR analysis on the BioMark System (Fluidigm). We thank C. Demur, V. De Mas (Centre Hospitalier Universitaire de Toulouse), and S. Lagarde for accessing and managing the patient samples, and A. Sarry for patient data collection. We thank all the clinicians of the Oncomip Network who referred their patients, all the nurses and other health care providers from the Hematology Department of the Toulouse University Hospital, and all the members of the GAEL (Gaël Adolescent Espoir Leucémie) association for their kind support to the patients. Funding: This work was supported by grants from La Ligue Nationale contre le Cancer (Equipe Labellisée 2013 to J.-S.H. and 2016 to S.M.), the Ligue Régionale Midi-Pyrénées 2014 (to S.M.), the Laboratoire d’Excellence Toulouse Cancer Labex TOUCAN grant (Integrative analysis of resistance in hematological cancers to J.-S.H. and S.M.), the Toulouse Cancer Santé Foundation (RESISTAML program to J.-E.S. and C.R.), the Canceropole Grand Sud-Ouest (projet Emergence 2012 to J.-S.H.), La Fondation de France (to A.F.-V.), and the “Plan Cancer 2014-2019” (to L.D.). Author contributions: A.F.-V., L.D., C.R., C.D., S.M., and J.-S.H. designed the experiments. A.F.-V., L.D., S.G., N.P., M.C., and C.L. performed the experiments. B.L. and D.D. performed the statistical analysis. A.F.-V., S.B., B.L., E.D., J.-E.S., S.M., C.C., C.R., and J.-S.H. analyzed the data and contributed to the manuscript preparation. C.R., C.D., S.M., and J.-S.H. supervised the project and wrote the manuscript. Competing interests: The authors declare the following patents that relate to this work: “New biomarkers for acute myeloid leukemia” (PCT/EP2015/053532, 19 February 2015 INSERM Transfert) (J.S.-H., C.C., A.F.-V., and C.R.) and “CHK1 and POLQ gene expression predicts AML patient survival” (EP14305231.4, 20 February 2014, INSERM Transfert) (J.S.-H., C.C., A.F.-V., and C.R.). Data and materials availability: Raw data of fluidigm analysis have been deposited in NCBI-Gene Expression Omnibus (GEO). Slope indices have been computed using the “rms” package for R (S Functions for Biostatistical/Epidemiologic Modeling, Testing, Estimation, Validation, Graphics, and Prediction; programs available from
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