Background: To improve the quality of care for patients with acute myeloid leukemia (AML), biomarkers predictive of response to the standard daunorubicin-based induction therapy are needed. Genetic variants affecting daunorubicin metabolism are attractive candidates for such biomarkers.

Methods: We have previously shown that 13 of the naturally occurring nonsynonymous single-nucleotide polymorphisms (SNP) in the reductase genes affect daunorubicin metabolism in vitro. Here, we test these SNPs individually and jointly for association with response to one cycle of daunorubicin-based chemotherapy in a sample of 189 patients with acute myelogenous leukemia.

Results: Of the 13 SNPs included in this study, only 5 passed quality control filters. No association was found between these 5 SNPs and response to one cycle of daunorubicin-based induction therapy in either individual or joint effect tests.

Conclusions: Despite their showing in vitro effect on metabolic rate of daunorubicin, the nonsynonymous SNPs in the reductase genes on their own are not significant contributors to the observed variability in response to daunorubicin therapy and thus, as singularities, are not useful biomarkers of this outcome.

Impact: The results of this investigation provide important information for studies on personalization of anthracycline-based therapies. Cancer Epidemiol Biomarkers Prev; 22(10); 1918–20. ©2013 AACR.

The standard treatment for acute myeloid leukemia (AML) involves induction therapy with a combination of daunorubicin and cytarabine (1, 2). Response to induction therapy is highly predictive of clinical outcome; unfortunately, it is also very variable with 20% to 50% of patients showing poor response (1, 2). Given the significant morbidity associated with the treatment, identification of patients who will not benefit can greatly improve quality of care. A number of cytogenetic and molecular alterations in the leukemic cells have been proven to have prognostic value (2). However, these do not explain all of the observed variability in response to treatment, suggesting that additional prognostic factors need to be identified.

The effectiveness of a drug is partly determined by its pharmacokinetics. There is a substantial interpatient variability in pharmacokinetics of daunorubicin, which is in large part a reflection of the conversion to the metabolite, daunorubicinol (3). This reaction is catalyzed by the aldo-keto and carbonyl reductases (AKR and CBR; refs. 4–6). We have previously shown that 13 of the naturally occurring nonsynonymous single-nucleotide polymorphisms (SNP) in the reductase genes reduce metabolism of anthracyclines in vitro (4–6). Here, we evaluate the SNPs with effect on in vitro daunorubicin metabolism for their association with response to daunorubicin-based induction therapy in a population of patients with AML.

All blood collections were conducted after informed consent and with approval from the Clinical Research Ethics Board of the University of British Columbia (BC, Canada). Clinical characteristics of the patient population as well as the definitions of complete remission and cytogenetic risk were described previously (1). From this patient cohort, 189 Caucasians receiving daunorubicin in combination with cytarabine (DN + ARAC) for the first cycle of remission induction were selected for this study (Table 1).

Table 1.

Acute myelogenous leukemia patient population characteristics

Risk groupNo. of patientsMalesFemalesAverage age, yAverage DN dose (mg/m2)No. of patients with IFTumor karyotypeNo. of patients
I 51 26 25 40 (14–70) 159 5 (10%) inv(16) 14 
       t(15;17) 30 
       t(8;21) 7 
II 121 57 64 49 (18–74) 135 45 (37%) (+8) 7 
       abn 11q23 6 
       del(7q) 2 
       del(9q) 2 
       Normal 79 
       Other 25 
III 17 9 8 46 (17–66) 135 9 (53%) (−5) 2 
       (−7) 2 
       abn 3q 1 
       del(5q) 4 
       Complex 8 
Total 189 92 97 46 (14–74) 141 59 (31%)   
Risk groupNo. of patientsMalesFemalesAverage age, yAverage DN dose (mg/m2)No. of patients with IFTumor karyotypeNo. of patients
I 51 26 25 40 (14–70) 159 5 (10%) inv(16) 14 
       t(15;17) 30 
       t(8;21) 7 
II 121 57 64 49 (18–74) 135 45 (37%) (+8) 7 
       abn 11q23 6 
       del(7q) 2 
       del(9q) 2 
       Normal 79 
       Other 25 
III 17 9 8 46 (17–66) 135 9 (53%) (−5) 2 
       (−7) 2 
       abn 3q 1 
       del(5q) 4 
       Complex 8 
Total 189 92 97 46 (14–74) 141 59 (31%)   

NOTE: Risk groups refer to cytogenetic risk and reflect favorable (risk group I), intermediate (risk group II), and unfavorable (risk group III) outcome.

Abbreviations: DN, daunorubicin; IF, induction failure.

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All patients were genotyped for 13 nonsynonymous SNPs in 4 AKR and 2 CBR genes (Table 2) using the Sequenom Genotyping System (Sequenom Inc.). As quality control, only SNPs with frequency above 1% and with genotyping rate more than 95% were included in statistical analysis (SNPs in bold in Table 2).

Table 2.

Nonsynonymous SNPs in reductase genes with shown in vitro effect on anthracycline metabolism

GeneSNPAmino acid changeFrequency in CEUaFrequency in AML population
CBR1 rs1143663 V88I 0.005 0.000 
 rs41557318 P131S 0.017 0.000 
CBR3 rs1056892 V244M 0.300 0.331 
 rs8133052 C4Y 0.536 0.470 
 rs2835285 V93I 0.017 <80% GR 
 rs4987121 M235L 0.000 0.000 
AKR1C3 rs35575889 R170C NA Failed genotyping 
 rs34186955 P180S NA Failed genotyping 
 rs4987102 A106T 0.000 0.000 
AKR1C4 rs17134592 L311V 0.158 0.149 
AKR7A2 rs1043657 A142T 0.097 0.087 
AKR1A1 rs6690497 E55D 0.000 0.000 
 rs2229540 N52S 0.044 0.058 
GeneSNPAmino acid changeFrequency in CEUaFrequency in AML population
CBR1 rs1143663 V88I 0.005 0.000 
 rs41557318 P131S 0.017 0.000 
CBR3 rs1056892 V244M 0.300 0.331 
 rs8133052 C4Y 0.536 0.470 
 rs2835285 V93I 0.017 <80% GR 
 rs4987121 M235L 0.000 0.000 
AKR1C3 rs35575889 R170C NA Failed genotyping 
 rs34186955 P180S NA Failed genotyping 
 rs4987102 A106T 0.000 0.000 
AKR1C4 rs17134592 L311V 0.158 0.149 
AKR7A2 rs1043657 A142T 0.097 0.087 
AKR1A1 rs6690497 E55D 0.000 0.000 
 rs2229540 N52S 0.044 0.058 

NOTE: SNPs in bold were tested for association with response to daunorubicin-based induction therapy.

Abbreviations: GR, genotyping rate; NA, not available.

aCEU, HapMap population representing Utah residents with Northern and Western European ancestry.

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Initially, a base model for predicting response to treatment from potential confounding variables was built using stepwise addition based on Akaike's Information Criterion. These included gender, age, and risk group as well as their two-way interaction terms.

The effect of individual SNPs was assessed with logistic regression-based likelihood ratio tests and the joint effect of all SNPs in response to treatment was assessed using a global test of association (7). All tests for SNPs effect also included SNP interaction terms with the nongenetic variables predictive of response to treatment. Correction for multiple testing was done using the Benjamini–Hochberg method.

The initial test for association of nongenetic variables with response to induction therapy identified only cytogenetic risk assignment (P = 8.28 × 10−4 and 5.52 × 10−4 for risk group II and III, respectively) as predictive of response to one cycle of induction therapy. Thus, all subsequent tests for effect of SNPs on treatment response were adjusted for cytogenetic risk group.

Of the 13 nonsynonymous SNPs included in the study, 5 passed quality control filters and were used in tests for association with response to one cycle of daunorubicin-based induction chemotherapy (SNPs in bold in Table 2). We first tested the 5 SNPs assuming additive SNP effect. The likelihood ratio tests for effect of each SNP individually found no association for any of the SNPs with response to induction therapy (P > 0.6). Furthermore, no association was found when all SNPs were tested for joint effect (P = 0.619) or when interaction with cytogenetic risk was included in the model (P = 0.648). We also repeated the tests for association with response to treatment assuming recessive SNP effect for rs1056892 and rs8133052, as these 2 SNPs had sufficient number of homozygotes to test the recessive model. There was no significant association detected for either one of the SNPs or their interaction with cytogenetic risk (P > 0.4) when the recessive model was applied. Similarly, no joint effect of the SNPs (P = 0.44) or their interaction with cytogenetic risk was detected under the recessive model (P = 0.297).

We have previously shown that a number of naturally occurring nonsynonymous SNPs in the reductase genes (AKRs and CBRs) have an effect on daunorubicin metabolism in vitro (4–6). Here, our goal was to test these for association with response to daunorubicin-based remission induction hoping that they could serve as biomarkers to improve treatment of patients with cancer. We found that, despite their significant in vitro effect on daunorubicin metabolism, none of the tested SNPs was associated with response to treatment in our study sample. Therefore, on its own, this set of SNPs is not useful for personalization of daunorubicin-based therapy.

No potential conflicts of interest were disclosed.

Conception and design: R. Reid, T.A. Grigliatti, W.K. Riggs

Development of methodology: J.M. Lubieniecka, J. Graham, R. Reid

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J.M. Lubieniecka, D. Heffner, D. Hogge, T.A. Grigliatti

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J.M. Lubieniecka, J. Liu, J. Graham, T.A. Grigliatti

Writing, review, and/or revision of the manuscript: J.M. Lubieniecka, J. Liu, J. Graham, D. Hogge, T.A. Grigliatti, W.K. Riggs

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.M. Lubieniecka, D. Heffner, R. Reid

Study supervision: R. Reid, T.A. Grigliatti

The work was supported by Canadian Institutes for Health Research (grant #21R45100) and Natural Sciences and Engineering Research Council of Canada (grant #222886-2007).

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©2013 American Association for Cancer Research.
2013