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Keywords:

  • acute myeloid leukaemia;
  • multi drug resistance gene;
  • multi drug resistance-related protein gene;
  • lung resistance protein gene;
  • prognostic factors

Summary

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

Multi drug resistance (MDR) is a major obstacle for cancer therapy. The three major candidates accounting for the development of MDR in acute myeloid leukaemia (AML) are multi drug resistance gene (MDR1), multi drug resistance-related protein gene (MRP1) and lung resistance protein gene (LRP). So far, the differential impact of resistance gene expression on treatment outcome in AML is not clear. Therefore, we examined MDR1, MRP1 and LRP gene expression at diagnosis in 331 adult AML patients in the context of other known prognostic factors, such as age, disease status, cytogenetics and FMS-like tyrosine kinase 3 (FLT3)-internal tandem duplication mutational status. Median observation time was longer than 5 years [64·1 months (40·0–87·6)]. MDR1 expression proved to be an independent prognostic factor for outcome of induction therapy (P < 0·001) and overall survival (P = 0·02), whereas MRP1 expression was an independent predictor for disease-free survival (P = 0·01) in the multivariate analysis. This prognostic impact of both resistance genes was also found in patients with intermediate risk cytogenetics. LRP expression, however, had no impact on treatment outcome in AML. Our study shows that resistance gene expression should be considered together with age, cytogenetics and FLT3 mutational status for risk-adapted treatment strategies in AML in the future.

The treatment outcome of patients with acute myeloid leukaemia (AML) varies considerably and reflects the heterogeneity of the disease. Therefore, prognostic factors have to be identified to improve treatment outcome by risk-adapted and individualized treatment approaches. Patient age and disease status (de novoversus secondary) were the first known prognostic factors in adult AML (Willman, 1999). In recent years, cytogenetics has been shown to be another major factor influencing treatment outcome. It was possible to discriminate between aberrations with favourable risk, such as t(8;21), inv(16) or t(15;17), and with unfavourable risk, such as −5, −7, abn(3) or multiple aberrations with impressive differences in treatment outcome (Grimwade et al, 1998; Slovak et al, 2000; Byrd et al, 2002).

However, about 50–60% of AML patients are classified as intermediate risk patients, which includes those with a normal karyotype or with cytogenetic aberrations not otherwise classifiable. Although these patients have an intermediate prognosis overall, treatment courses and results vary considerably. Thus, other prognostic factors are needed to enable a more detailed classification. The first step in this direction was performed by identifying FMS-like tyrosine kinase 3 (FLT3) mutations being associated with an adverse outcome in AML (Kottaridis et al, 2001; Thiede et al, 2002).

In this context, overexpression of resistance genes, such as multi drug resistance gene (MDR1), multi drug resistance-related protein gene (MRP1) and lung resistance protein gene (LRP), is a frequent phenomenon in adult AML and may serve as an additional prognostic marker. These three resistance genes are frequently co-expressed in AML blasts (Beck et al, 1996; Leith et al, 1999; Galimberti et al, 2003a) and expression of those genes in tumour cells confers resistance towards a broad variety of cytotoxic drugs used in AML therapy in vitro (Simon & Schindler, 1994; List, 1996; Raaijmakers et al, 1998). However, the in vivo data are not so clear. Whereas most studies performed to date in AML showed an influence of MDR1/p-glycoprotein expression on remission rate, its prognostic value for survival is still a matter of debate (Lamy et al, 1994; Leith et al, 1997, 1999; van den Heuvel-Eibrink et al, 1997).

For MRP1 or LRP expression, the controversy regarding prognostic impact is even more pronounced (Hunault et al, 1997; Michieli et al, 1997; Borg et al, 1998; Filipits et al, 1998; Legrand et al, 1999; Leith et al, 1999). The reasons for these divergences may be because of small patient numbers in some studies, missing cytogenetic data for performing valid multivariate analyses or comparison of patients who were treated according to different protocols. Furthermore, so far only limited data exist regarding the influence of resistance gene expression on prognosis together with FLT3 mutational status (Galimberti et al, 2003b).

Therefore, we performed a multivariate analysis in a large number of AML patients including all known major risk factors, especially FLT3 mutational status and expression of the three resistance genes MDR1, MRP1 and LRP. All patients had karyotype analysis and the median observation time was longer than 5 years.

Patients

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

A total of 331 adult patients with de novo or secondary AML were studied. All of them were included between February 1996 and February 2000, and treated within the German multicentre treatment trial of the Süddeutsche Hämoblastose-Gruppe (SHG AML96). Median observation time was 64·1 months (40·0–87·6). Patient characteristics are listed in Table I. Patients diagnosed with the French–American–British (FAB) subtype M3 were not included and treated within other European trials. The treatment schedule of the SHG AML96 trial has been previously published (Schaich et al, 2001).

Table I.  Characteristics of all examined acute myeloid leukaemia (AML) patients.
  1. *Data of FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD) mutant/wt ratio were available of 318 patients.

  2. †Cytogenetic risk groups were defined according to the protocol of the SHG AML96 study group with t(8;21) as favourable, 5/del(5q), −7/del(7q), hypodiploid karyotypes (besides 45,X,-Y or −X), inv(3q), abnl12p, abnl11q,+11, +13, +21, +22, t(6;9); t(9;22); t(9;11); t(3;3), multiple aberrations as unfavourable. All other aberrations and normal karyotypes were regarded as intermediate.

Age (years)
 Median (range)56 (15–78)
 ≤60 years (n)208
 >60 years (n)123
Disease status (n)
 De novo276
 Secondary
  Post-MDS44
  Therapy-related11
White blood cell count (×109/l)
 Median (range)26·3 (0·8–380·0)
Bone marrow blasts (%)
 Median (range)66 (30–99)
Resistance gene expression (n)
 MDR1 positive83
 MRP1 positive149
 LRP positive231
CD34 expression (n)
 Positive160
Lactate dehydrogenase (IU/l)
 Median (range)948 (258–8958)
FLT3-ITD mutant/wild type ratio (n)*
 >0·8035
FAB subtype (n)
 M015
 M174
 M2105
 M458
 M5a49
 M5b20
 M65
 M75
Cytogenetics (n)†
 Favourable18
 Intermediate233
 Unfavourable80

Briefly, double induction therapy was stratified according to age. Patients ≤60 years received one course of mitoxantrone 10 mg/m2 days 4–8, cytosine arabinoside 100 mg/m2 days 1–8 and VP-16 (etoposide) 100 mg/m2 days 4–8, and a second course of cytosine arabinoside 2 × 1000 mg/m2 days 1–5 and m-amsacrine 100 mg/m2 days 1–5.

Patients >60 years were treated with two courses of daunorubicin 45 mg/m2 days 3–5 and cytosine arabinoside 100 mg/m2 days 1–7).

Complete remission (CR) was defined as the presence of <5% of blast cells in a standardized bone marrow puncture after the second course of induction therapy. Only patients with a fully regenerated peripheral blood count were considered to be in CR. Postremission therapy for individuals ≤60 years was priority-based and adapted for cytogenetic risk (Schaich et al, 2001).

The study was approved by the ethics committee of the University of Dresden. Each patient gave written informed consent. The control group for drug resistance gene expression consisted of 12 healthy bone marrow donors.

Sample handling

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

Bone marrow or peripheral blood samples were taken at diagnosis. Samples were divided for routine analysis (determination of FAB-type and blast count), cytogenetics and immunocytochemical analysis. One aliquot of the sample was frozen in liquid nitrogen and samples were thawed according to routine protocols immediately prior to RNA extraction. Probes containing <80% of myeloblasts were referred for CD-3 depletion. We performed depletion using CD-3-coated dynabeads (Dynal, Hamburg, Germany) according to the manufacturers’ recommendations. CD-3 positive cells were eliminated with a sensitivity of 98% (data not shown).

Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

Ribonucleic acid extraction, cDNA synthesis and PCR analysis was performed as previously described (Schaich et al, 2001). Briefly, a final volume of 50 μl was used containing 1x reaction buffer (Perkin Elmer-Applied Biosystems, Weiterstadt, Germany), 2·0 mmol/l MgCl2, 15 pmol of each primer, 200 μmol/l each dNTP kit (Pharmacia, Freiburg, Germany) and 1·5 U AmpliTaqGold-Polymerase (Perkin Elmer-Applied Biosystems). GAPDH primers were obtained from Clontech (Heidelberg, Germany). MDR1 and MRP1 oligonucleotides were used as previously described (Beck et al, 1995). LRP primers were derived from the published sequence of the LRP gene (GenBank accession number X79882) and were as follows: 5′-CGC TGC TTG ATT TTG AGG AT and 5′-CGA GAA TCA CGC AGT AGT TG. The primer pairs were tested in cycle kinetic analysis in order to ensure amplification in the exponential range of PCR (data not shown). All PCR reactions were run at least twice. GAPDH, MDR1, MRP1 and LRP PCR reaction products were ethanol-precipitated and subsequently loaded on a 5% polyacrylamide gel. After electrophoresis, the gel was ethidium bromide stained and evaluated using the BioDoc II video documentation system (Biometra, Göttingen, Germany). Densitometrical analysis was performed with the ScanPackTM 3.0 software (Biometra). The area under the curve was evaluated for each amplified gene product. Division with the observed GAPDH-value determined the relative amounts of resistance gene expression. The accuracy of PCR amplification was controlled using CCRF-VCR100 for MDR1 and MRP1 (Beck et al, 1995), and HT29 for LRP (Izquierdo et al, 1996) as published positive reference cell lines. The relative resistance gene expression values of the patients were compared with the mean value of the relative expression intensities of 12 bone marrow probes of healthy donors. Expression values were considered positive for one resistance gene if the patient value was higher than the mean value of healthy donors. The probes of the control group were T-cell depleted, were handled in exactly the same way as the probes of the study population and consisted of previously taken bone marrow aspirates.

FLT3-activating mutations

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

Deoxyribonucleic acid was extracted by the use of either phenol/chloroform or a silica-based procedure according to the manufacturers’ instructions (Quiagen DNA Blood Kit; Quiagen, Hilden, Germany).

The results of the FLT3 analysis have been published recently (Thiede et al, 2002). Briefly, FLT3 internal tandem duplication (FLT3-ITD) mutations were screened by PCR for exons 14 and 15 with published primers 11F and 12R (Kiyoi et al, 1999). To identify the mutant/wild-type FLT3 ratio, a Genescan analysis with 6-carboxyfluorescein (6-FAM)-labelled FLT3 11F primer (TIB MOLBIOL, Berlin, Germany) was performed. The PCR for Genescan analysis was run using 5 ng template DNA and AmpliTaq Gold DNA-polymerase. PCR conditions were as follows: preincubation at 95°C for 11 min followed by 30 s at 94°C, 30 s at 57°C and 60 s at 72°C for 27 cycles, and a final elongation step at 60°C for 45 min to achieve quantitative addition of +A overhangs (Clark, 1988).

As in our previously published study (Thiede et al, 2002), the FLT3 mutant/wild-type ratio >0·80 was shown to be a highly significant prognostic factor for treatment outcome in AML, we used this ratio and threshold for further analysis.

For sequence analysis of the FLT3-ITD mutations, PCR products were separated on 3% agarose gels, the mutant bands were isolated and cloned into pCR 2·1 TOPO vectors (Invitrogen, Leek, The Netherlands). The plasmid DNA was then sequenced on an ABI 377 sequencer using Big Dye Terminator cycle sequencing chemistry (Applied Biosystems). Sequences were compared with the wild-type sequence (GenBank accession number, NM_004119).

Flow-cytometry

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

For the discrimination of CD34 positive cells, CD34 monoclonal antibody QBEnd10 (Coulter-Immunotech Diagnostics, Hamburg, Germany) was used according to previously published protocols (Gramatzki et al, 1998). CD34 positivity was defined as ≥20% CD34 positive blast cells within the examined blast samples.

Cytogenetics

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

Chromosome analyses were performed in all the 331 AML patients studied and were performed on metaphases from direct preparations, as well as from 24 and 48 h cultures of bone marrow and/or peripheral blood samples as described previously (Mohr et al, 2000). The cytogenetic preparation and G-banding were performed according to the routine laboratory procedures.

Cytogenetic risk groups were defined as follows – unfavourable: −5/del(5q), −7/del(7q), hypodiploid karyotypes (besides 45,X,-Y or –X), inv(3q), abnl12p, abnl11q,+11, +13, +21, +22, t(6;9); t(9;22); t(9;11); t(3;3), multiple aberrations; intermediate: patients without low risk or high risk constellation; and favourable: t(8;21) and t(8;21) combined with other aberrations.

Statistical analysis

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

Basic statistical data, such as mean values, standard deviations (SD) and frequencies were obtained using the statistical package for the social sciences (spss) software (SPSS Inc., Chicago, IL, USA). Differences in MDR1 gene expression between the analysed cytogenetic subgroups and univariate analyses of the correlation between experimental findings, and response to induction therapy were evaluated by two-tailed Fisher's exact test. Multivariate analyses of the correlation between experimental or clinical parameters and therapy response were carried out by stepwise logistic regression. Multivariate analyses of the correlation between experimental or clinical parameters and survival were carried out by Cox regression.

Overall and disease-free survival analyses were performed using the Kaplan–Meier method and survival curves were compared using the log-rank test. P < 0·05 were considered significant.

Resistance gene expression in AML

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

Blast probes of 331 patients with AML, independent of age and disease status, were examined. Patients characteristics are shown in Table I.

Eighty-three patients (25%) had a relative MDR1 expression, exceeding the threshold of normal bone marrow, and were regarded as MDR1-positive. MRP1 positivity was found in 149 (45%) and 231 patients (70%) were positive for LRP.

Correlations between the expressions of the examined resistance genes were found for MDR1 and MRP1 (P < 0·001), as well as for MRP1 and LRP (P = 0·002).

Patients older than the median age of 56 years were more likely to be MDR1-positive than their younger counterparts (31% vs. 19%; P = 0·01). MDR1-positive patients had lower white blood cell (WBC) counts (P < 0·001), lactate dehydrogenase (LDH) levels (P < 0·001) and bone marrow blasts (P = 0·004) at diagnosis than MDR1-negative patients. Eighty-seven per cent of MDR1-positive patients displayed CD34 expression on their blasts, whereas this was only true for 41% of MDR1-negative patients (P < 0·001). Furthermore, monocytic FAB subtypes M4, M5a and M5b had significantly lower expression frequencies than subtype M2 (P = 0·004, 0·001 and 0·026 respectively).

A similar pattern was found for MRP1, with lower WBC (P = 0·03) and LDH levels (P = 0·025) in MRP1-positive compared with MRP1-negative patients, correlation of MRP1 and CD34 expression (P < 0·001), and lower MRP1 expression frequencies in FAB subtypes M4 (P = 0·001), M5a (P = 0·001) and M5b (P = 0·001).

However, for LRP expression, no differences could be seen for age, WBC, LDH, blast count or FAB subtypes. We only noticed an inverse correlation of LRP1 and CD34 expression (P = 0·02).

Patients with post-MDS secondary AML had a higher MDR1 expression rate compared with de novo AML patients (41% vs. 23%; P = 0·02). For the other two resistance genes, no significant expression differences were seen between de novo and secondary AML patients.

MDR1 expression frequency was different between the cytogenetic risk groups. Thirty-two of 80 patients (40%) with unfavourable and nine of 18 patients (50%) with favourable risk karyotype were MDR1-positive compared with only 42 of 233 patients (18%) with intermediate risk karyotype (P < 0·001 and P = 0·003 respectively). However, no significant differences were found for MRP1 or LRP expression within the cytogenetic risk groups.

Data on FLT3-ITD mutant/wild-type ratio were available for 318 patients. Thirty-five (11%) patients had a ratio >0·80. Interestingly, MDR1-positive patients had significantly less FLT3-ITD mutations than MDR1-negative patients (9% vs. 30%; P < 0·001). In contrast, MRP1-positive patients had a higher frequency of FLT3-ITD mutations compared with MRP1-negative patients (30% vs. 20%; P = 0·05).

Influence of resistance gene expression on treatment outcome

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

MDR1 expression had a significant influence on outcome of induction therapy. Fifty-six per cent of MDR1-negative compared with 31% of MDR1-positive patients reached CR criteria after double induction therapy (P < 0·001). CR rates were not different between MRP1- or LRP-negative and -positive patients (52% vs. 46%; P = 0·38 and 52% vs. 48%; P = 0·55 respectively).

In the multivariate analysis, MDR1 expression proved to be the strongest independent prognostic factor for treatment failure (P < 0·001) (see Table II).

Table II.  Prognostic factors for outcome of induction therapy (CR-rate) in all examined acute myeloid leukaemia (AML) patients: multivariate analysis.
 Stepwise forward logistic regression analysis
RP95% CIP-value
  1. CR, complete remission; RP, relative probability to reach CR criteria; CI, confidence interval; wt, wild type.

  2. *The cut-off level was 60 years for age and 0·80 for a high FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD) mutant/wt ratio.

  3. †Disease status summarizes secondary versusde novo AML.

  4. ‡Cytogenetic risk groups were defined according to the protocol of the SHG AML96 study group with t(8;21) as favourable, 5/del(5q), −7/del(7q), hypodiploid karyotypes (besides 45,X,−Y or −X), inv(3q), abnl12p, abnl11q,+11, +13, +21, +22, t(6;9); t(9;22); t(9;11); t(3;3), multiple aberrations as unfavourable.

Age*0·280·17–0·47<0·001
Disease status†0·360·17–0·770·008
MDR1 expression0·250·13–0·49<0·001
MRP1 expression0·46
LRP expression0·87
FLT3-ITD mutant/wt ratio*0·380·17–0·830·02
Favourable cytogenetics‡25·413·04–212·630·003
Unfavourable cytogenetics‡0·35

Furthermore, MDR1 expression had independent prognostic influence on overall survival in the multivariate analysis including all relevant prognostic criteria known in AML, like age, disease status, FLT3 mutations and cytogenetics (see Table III).

Table III.  Prognostic factors for survival in all examined acute myeloid leukaemia (AML) patients: multivariate analysis.
 Stepwise forward Cox regression analysis
Overall survivalDisease-free survival
RP95% CIP-valueRP95% CIP-value
  1. CR, complete remission; RP, relative probability to reach CR criteria; CI, confidence interval; wt, wild type.

  2. *The cut-off level was 60 years for age and 0·80 for a high FMS-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD) mutant/wt ratio.

  3. †Disease status summarizes secondary versusde novo AML.

  4. ‡Cytogenetic risk groups were defined according to the protocol of the SHG AML96 study group with t(8;21) as favourable, 5/del(5q), −7/del(7q), hypodiploid karyotypes (besides 45,X,−Y or −X), inv(3q), abnl12p, abnl11q,+11, +13, +21, +22, t(6;9); t(9;22); t(9;11); t(3;3), multiple aberrations as unfavourable.

Age*1·831·40–2·39<0·0011·841·21–2·800·004
Disease status†0·330·57
MDR1 expression1·441·07–1·950·020·93
MRP1 expression0·331·681·13–2·500·01
LRP expression0·950·75
FLT3-ITD mutant/wt ratio*2·381·61–3·52<0·0012·731·45–5·160·002
Favourable cytogenetics‡0·400·20–0·830·010·460·21–1·010·05
Unfavourable cytogenetics‡ 0·071·701·05–2·750·03

This led to a worse overall survival of MDR1-positive patients compared with that of their MDR1-negative counterparts (13% vs. 29% after 6 years; P = 0·03) (see Fig 1).

image

Figure 1. Overall survival of MDR1 positive (solid line) versus negative (dashed line) acute myeloid leukaemia patients.

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Interestingly, MRP1 proved to be an independent prognostic factor for disease-free survival (P = 0·01) together with unfavourable risk cytogenetics (P = 0·03), age (P = 0·004) and FLT3-ITD ratio (P = 0·002) (see Table III).

This resulted in a 10% difference in disease-free survival between MRP1-negative and -positive patients after 6 years (P < 0·05) (see Fig 2). The median disease-free survival was 24·2 months for MRP1-negative patients compared with 11·3 months for MRP1-positive patients.

image

Figure 2. Disease-free survival of MRP1 positive (solid line) versus negative (dashed line) acute myeloid leukaemia patients.

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These differences were because of a shorter time to relapse in MRP1-positive patients (median 11·3 vs. 25·0 months, P = 0·02). The probability of achieving a second CR was not different between MRP1-positive and -negative patients (30% vs. 35%; P = 0·81).

Looking only at patients with intermediate risk cytogenetics, MDR1 kept its independent prognostic value for induction treatment response [relative probability (RP) 0·37 (0·17–0·83); P = 0·02] and overall survival [RP 1·67 (1·13–2·48); P = 0·009], whereas MRP1 had significant influence on disease-free survival in the multivariate analysis [RP 1·73 (1·08–2·76); P = 0·02].

As age (with a cut-off level of 60 years) was the strongest independent predictor for treatment outcome in all AML patients, and as treatment was different between patients ≤60 years old and >60 years old, we additionally analysed both age groups separately.

In patients ≤60 years old, MDR1 expression proved to be an independent negative prognostic factor for outcome of induction therapy and overall survival. The RP of CR was 0·24 (0·11–0·54) (P < 0·001) and of death 1·75 (1·17–2·61) (P = 0·007) for MDR1-positive patients.

Furthermore, MRP1 had independent prognostic impact on disease-free survival in younger patients, with an RP of relapse or death of 1·79 (1·11–2·88) (P = 0·02) for MRP1-positive patients.

In patients >60 years old, MDR1 expression showed a trend towards an independent influence on the outcome of induction therapy (P = 0·07). However, no independent prognostic impact of MDR1 or MRP1 expression on overall and disease-free survival could be determined in this group of patients.

Discussion

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

The present analysis found that MDR1 expression was an independent prognostic factor for induction therapy outcome. The probability of achieving CR was significantly lower for MDR1-positive patients compared with their MDR1-negative counterparts. This is because of an increased resistance to apoptosis in MDR1/p-glycoprotein positive blast cells (Pallis et al, 2003). Furthermore MDR1 expression proved to have independent influence on overall survival despite the correlation of MDR1 expression with high-risk cytogenetic aberrations. This finding is in accordance to previously published data by two other groups (Samdani et al, 1996; Wuchter et al, 1999).

Therefore, MDR1 overexpression may not only be a secondary event activated by specific aberrations in cytogenetically unstable AML patients but seems to have additive effects on prognosis. Recently, our group could show highly significant correlations of MDR1 expression and the appearance of the high-risk aberrations like del(7q), −7, del(5q) or abn(3q).Within those specific cytogenetic aberrations with a bad prognosis, MDR1-negative patients had treatment response rates comparable with that of patients with normal karyotype (Schaich et al, 2002).

To date, the literature has reported only anecdotal data about MRP1 or LRP expression within cytogenetic subgroups of AML patients. Leith et al (1999) reported about eight MRP1-positive patients with evaluable cytogenetics; half of them with intermediate and the other half with favourable karyotypes. The same group presented 72 LRP-positive patients with a correlation to favourable cytogenetics. In our series of 331 patients no association of MRP1 or LRP expression to cytogenetics was obvious.

Interestingly, MRP1 expression was an independent prognostic factor for disease-free survival because of a shorter remission time for MRP1-positive patients. Recently, Laupeze et al (2002) found a shorter overall survival for MRP1-positive patients. As they had only 44 patients in their survey, multivariate analysis was not possible. Furthermore, low Daunorubicin levels at diagnosis in blast cells because of MDR1 and MRP expression resulted in a trend towards earlier relapse in a series of 69 AML patients (Borg et al, 2000). Small patient numbers and different methodological approaches may be the reasons for the lack of significance of MRP1 expression in two other published studies (Filipits et al, 1997; Leith et al, 1999).

The lack of influence of MRP1 expression on the remission rate in our series is not in contrast to the influence that was found on disease-free survival. For most of the AML patients, i.e. patients younger than 60 years of age, mitoxantrone was used within the induction therapy. Mitoxantrone is known to circumvent MRP1-mediated resistance (Lautier et al, 1996). Thus, an initial good response to induction therapy is possible. The shorter remission duration in MRP1-positive patients may be the result of a selection of high MRP1-expressing residual blast cells as origin for relapse. Indeed van der Kolk et al (2001) were able to show, in paired AML blast samples from diagnosis and relapse, an increased MRP1 activity in 40% of the samples at relapse.

When patients aged ≤60 years and patients aged >60 years were analysed separately, a significant independent influence of MDR1 and MRP1 on treatment outcome was still present in the younger patients. For older patients, however, resistance gene expression lost significance. The main reason for this may be that age >60 years is the most important independent prognostic factor in AML, with a generally dismal prognosis in this patient group. With an overall CR rate of only 31% in this patient group, MDR1 or MRP1 expression do not add further significant negative prognostic impact. Nevertheless, the differences in treatment strategy of both age groups could potentially have altered the influence of resistance gene expression in elderly, compared with younger, AML patients in our study. However, elderly patients received daunorubicin and younger patients mitoxantrone within induction therapy. As MDR1 and MRP1 expression confer less resistance towards mitoxantrone when compared with daunorubicin (Norgaard et al, 1998; Koo et al, 1999), this differential resistance might not explain the different prognostic influence of both resistance genes between the age groups.

LRP expression was not found to have any influence on treatment outcome. Recently, LRP has evolved rather as a marker of monocytic lineage than a prognostic marker in AML (Sunnaram et al, 2003). However, as the intracellular distribution of the LRP vault protein has influence on the detection and function (Wiemer et al, 1998), comparison with other studies is very difficult. Technical aspects seem to play a major role in this context. Protein-based techniques detect mainly the membrane-bound protein, whereas the intracellular vesicular protein is normally missed (Legrand et al, 1998). This could explain the higher LRP expression levels in our survey compared with protein-based studies. Overall, the impact of LRP expression on treatment outcome is still as unclear as its real function in blast cells.

Finally, the present study examined the influence of resistance gene expression in the context of FLT3-mutational status, known to be the strongest prognostic factor in AML besides age and cytogenetics. Although the FLT3 mutant/wt ratio proved to be a strong prognostic factor in the present study, MDR1 and MRP1 expression had independent influence on treatment outcome. This seems to be even more interesting as patients with FLT3 mutations were less frequently MDR1 positive, possibly because of a loss of the MDR1 phenotype under increased proliferative capacity, as reported by Smeets et al (1999). Therefore, some MDR1 positive/FLT3 negative patients with intermediate risk karyotype add to the group of patients with adverse prognosis and refine the AML risk classification.

This is in accordance with a recently published study of Galimberti et al (2003b), who found no significant correlation between FLT3 mutations and high levels of MDR1-mRNA in 61 AML patients.

Furthermore, the positive correlation between MRP1 expression and FLT3 mutations could point to a MRP resistance pathway in FLT3-positive blast cells giving rise to residual blast cells with high capacity for relapsing disease.

In summary, MDR1 is an important prognostic factor for induction therapy outcome and overall survival. MDR1-positive AML patients should therefore receive induction regimens, in which classical MDR drugs such as daunorubicin are replaced by drugs such as idarubicin which can circumvent the MDR phenomenon. This approach is supported by the finding of Tsimberidou et al (2002) who reported a limited role of MDR transporter proteins for treatment failure after idarubicin-based induction therapies.

An important finding of the present study is the independent influence of MRP1 on remission duration. This seems even more important in the light of new data that showed that MRP1 conferred resistance against immuno-conjugates, like gemtuzumab ozogamicin (Walter et al, 2003), especially if it is kept in mind that such drugs are considered for the treatment of relapsed disease. MRP1-positive AML patients might benefit from more intensified consolidation strategies, such as autologous or allogeneic stem-cell transplantation. The value of such intensified approaches has to be prospectively analysed in future studies. Moreover, studies are under way to clarify the impact of FLT3-modulating agents on resistance parameters, like MDR1 or MRP1.

The presented data provide a rational for a more specific risk calculation in adult AML using MDR1 and MRP1 expression together with age, disease status, cytogenetics, FLT3 mutations and other newly evaluated prognostic factors such as blast clearance at day 15 (Kern et al, 2003). This will lead to more individualized treatment approaches to finally improve the overall treatment outcome of patients with AML.

Acknowledgements

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References

The study was partly supported by grants from the Deutsche Krebshilfe to G.E.

We thank the following physicians of the German SHG AML96 study group who entered their patients into the trial:

D. Huhn, O. Knigge (Universitätsklinikum Charité, Berlin); E. Späth-Schwalbe, S. Hesse-Amojo (Krankenhaus Spandau, Berlin); O. Rick, W. Siegert (Charité Campus Mitte, Berlin); R. Kolloch, U. Krümpelmann (Krankenanstalten Gilead, Bielefeld); K.-H. Pflüger, T. Wolff (Evang. Diakonissenanstalt Bremen); H.-H. Heidtmann (St Joseph-Hospital, Bremerhaven); F. Marquard (Allgemeines Krankenhaus, Celle); F. Fiedler, R. Herbst (Krankenhaus Küchwald, Chemnitz); M. Gramatzki, G. Helm (Universitätsklinikum, Erlangen); J.-G. Saal (Malteser Krankenhaus, Flensburg); H.-G. Höffkes, M. Arland (Städtisches Klinikum, Fulda); E. Faßhauer (St Elisabeth-Krankenhaus, Halle); N. Schmitz (Allgemeines Krankenhaus St Georg, Hamburg); H. Schmidt, K. Buhrmann (Kreiskrankenhaus, Hameln); H. Dürk (St Marien-Hospital, Hamm); M. Burk (Klinikum Stadt, Hanau); A.-D. Ho (Universitätsklinikum, Heidelberg); A. Bartholomäus (St Bernward Krankenhaus, Hildesheim); A. A. Fauser (Klinik f. Hämatologie/ Onkologie und KMT, Idar-Oberstein); H. Link, F.-G. Hagmann (Westpfalzklinikum, Kaiserslautern); G. Köchling (Kreiskrankenhaus, Leer); K.-P. Schalk (St Vincent-Krankenhaus, Limburg/Lahn); S. Fetscher (Städtisches Krankenhaus Süd, Lübeck); T. Wagner (Universitätsklinikum, Lübeck); A. Neubauer (Universitätsklinikum, Marburg); H. Bodenstein, J. Tischler (Klinikum Minden, Minden); H. Pohlmann, N. Brack (Städtisches Krankenhaus München-Harlaching, München); H. Wandt, K. Schäfer-Eckart, T. Denzel (Städtisches Klinikum, Nürnberg); B. Seeber (Klinikum Offenbach, Offenbach); F. Hirsch (Kreiskrankenhaus, Offenburg); T. Geer, H. Heißmeyer (Diakonie-Krankenhaus, Schwäbisch-Hall); J. Labenz (Ev. Jung-Stilling-Krankenhaus, Siegen); J. Kaesberger (Diakonissen-Krankenhaus, Stuttgart); W. E. Aulitzky, L. Leimer (Robert-Bosch-Krankenhaus, Stuttgart); M. R. Clemens, R. Mahlberg (Mutterhaus der Borromaerinnen, Trier); R. Schwerdtfeger (Deutsche Klinik für Diagnostik, Wiesbaden); R. Engberding, R. Winter (Stadtkrankenhaus, Wolfsburg); M. Sandmann (Klinikum St. Antonius, Wuppertal); M. Wilhelm, F. Weissinger, H. Rückle-Lanz (Universitätsklinikum, Würzburg).

References

  1. Top of page
  2. Summary
  3. Patients and methods
  4. Patients
  5. Sample handling
  6. Reverse transcription polymerase chain reaction (RT-PCR) for MDR1, MRP1 and LRP gene expression
  7. FLT3-activating mutations
  8. Flow-cytometry
  9. Cytogenetics
  10. Statistical analysis
  11. Results
  12. Resistance gene expression in AML
  13. Influence of resistance gene expression on treatment outcome
  14. Discussion
  15. Acknowledgements
  16. References
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