SEARCH

SEARCH BY CITATION

Keywords:

  • acute myeloid leukaemia;
  • chemoresistance;
  • p-glycoprotein

Summary

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

The acute myeloid leukaemia (AML)14 trial addressed four therapeutic questions in patients predominantly aged over 60 years with AML and High Risk Myelodysplastic Syndrome: (i) Daunorubicin 50 mg/m2 vs. 35 mg/m2; (ii) Cytarabine 200 mg/m2 vs. 400 mg/m2 in two courses of DA induction; (iii) for part of the trial, patients allocated Daunorubicin 35 mg/m2 were also randomized to receive, or not, the multidrug resistance modulator PSC-833 in a 1:1:1 randomization; and (iv) a total of three versus four courses of treatment. A total of 1273 patients were recruited. The response rate was 62% (complete remission 54%, complete remission without platelet/neutrophil recovery 8%); 5-year survival was 12%. No benefits were observed in either dose escalation randomization, or from a fourth course of treatment. There was a trend for inferior response in the PSC-833 arm due to deaths in induction. Multivariable analysis identified cytogenetics, presenting white blood count, age and secondary disease as the main predictors of outcome. Although patients with high Pgp expression and function had worse response and survival, this was not an independent prognostic factor, and was not modified by PSC-833. In conclusion, these four interventions have not improved outcomes in older patients. New agents need to be explored and novel trial designs are required to maximise prospects of achieving timely progress.

Progress in the treatment of Acute Myeloid Leukaemia (AML) and high risk Myelodysplastic Syndrome (MDS) in older patients in recent years has been disappointing. In our experience, and that of others, complete remission (CR) of disease can be achieved with a number of chemotherapy combinations, but there is little evidence of improvements in 5-year survival over the last 20 years (Burnett & Mohite, 2006). This is in contrast to the general experience in younger patients, for which a number of reasons have been suggested. In addition, there is increasing recognition that a substantial proportion of older patients are not offered or decline an intensive treatment approach (Taylor et al, 1995; Menzin et al, 2002). These observations have a number of implications. For example, recruitment to trials must therefore be selective so that the results of clinical trials in this age group cannot be extrapolated to every patient who presents. Insufficient attention has been paid to the development of treatments that are more suitable for the many older patients who may only be offered supportive care. It is conceivable that some patients who are unlikely to benefit from an intensive approach have their survival shortened by an intensive approach, and they may have had better survival with less aggressive treatment. Previous studies of this issue demonstrated that intensive chemotherapy was able to achieve a higher response rate, and overall require less supportive care, but that there was no overall difference in survival (Lowenberg et al, 1989; Tilly et al, 1990). However these studies were not large enough to determine which subgroups of patients might benefit from an intensive or a non-intensive treatment approach.

Collaborative group trials have seldom offered a non-intensive treatment option to patients who are considered unlikely to benefit from an intensive approach. This AML14 trial intended to evaluate treatment options both for patients who were considered likely to benefit from conventional chemotherapy, and also for patients for whom a non-intensive treatment approach was considered appropriate. It was intended that where there was uncertainty as to which approach to take, patients would be randomized to one or other strategy. With sufficiently large recruitment it was intended to identify subgroups of patients who would preferentially benefit from one or other treatment approach.

Chemo-resistance is a feature in older patients who have been treated with intensive chemotherapy. Although there may be several mechanisms of resistance, P glycoprotein (Pgp), a product of the ABCB1 (MDR1) gene, has been a prime target because of its frequent over-expression in older patients, its relationship with an inferior response to induction, a higher risk of relapse and, in some studies, a poorer overall survival (OS) (Campos et al, 1992; Poeta et al, 1996; van den Heuvel-Eibrink et al, 1997; Hunault et al, 1997; Leith et al, 1999; Filipits et al, 2000). At the start of AML14, there was some clinical information to suggest that this approach could provide clinical benefit. In particular, a study in high risk patients from the Southwest Oncology Group using ciclosporin, as a modulator of Pgp resistance, given by continuous infusion showed a superior response rate and OS in relapsed patients (List et al, 2001). In our Medical Research Council (MRC) trial for refractory/relapsed AML (AML-R) however, we were unable to show a benefit in relapsed patients using ciclosporin, although the ciclosporin levels that were achieved were probably insufficient (Yin et al, 2001). Although effective in vitro, ciclosporin was not an ideal choice as a modulator because of its immunosuppressive properties and its potential for nephrotoxicity in older patients who may also receive potentially nephrotoxic antibiotics. In collaboration with the Dutch HOVON group, we conducted a feasibility study of the ciclosporin analogue, PSC-833, which had the potential benefit of less immunosuppression and renal toxicity. The aim of this feasibility study was to determine the dose of daunorubicin that could be used in combination with PSC-833, which, like ciclosporin, delays the excretion of daunorubicin and thus increases the Area Under the Curve (AUC) of daunorubicin with the potential to increase toxicity. This study established that a reduced daunorubicin dose level of 35 mg/m2 (D35) was safe (Sonneveld et al, 2000) and set the stage for a potential registration study where patients aged over 60 years were randomized to receive a daunorubicin plus Cytosine Arabinoside (Ara-C) (3 + 7) schedule which compared daunorubicin 35 mg/m2 combined with PSC-833 with daunorubicin 50 mg/m2 (D50). No differences were observed between the arms with respect to remission rate, disease-free or OS (Holt et al, 2005). One possible interpretation of that finding was that PSC-833 compensated for the reduced efficacy of the lowered dose of daunorubicin. Alternatively, PSC-833 may have no effect because there was no difference in efficacy between a daunorubicin dose of 35 and 50 mg/m2. In an effort to resolve this issue, the AML 14 trial randomized patients to one of two daunorubicin doses (50 mg/m2 vs. 35 mg/m2) in a daunorubicin/Ara-C/thioguanine combination. In addition, for the period when PSC-833 was available, patients were randomized in a 1:1:1 fashion between D50, D35 and D35 + PSC. Where clinicians or patients were not inclined to randomize, for whatever reason, the patient was to receive daunorubicin at 50 mg/m2. The PSC-833 randomization was closed early because of drug unavailability, and because there was a trend for an increase in early deaths, so for the remainder of the trial, patients were randomized between the two doses of daunorubicin.

Prospective trials in younger patients have addressed the question of whether it is of benefit to substantially increase the dose of Ara-C in induction, with inconclusive results (Bishop et al, 1996; Weick et al, 1996). Because similar large doses are not feasible in older patients due to an increased risk of neurotoxicity, and because of the observation in the landmark trial conducted by the Cancer and Leukaemia Group B that there was no significant difference in outcome between consolidation with an Ara-C dose of 3 g/m2 and a 400 mg/m2 (A400) dose (Mayer et al, 1994), we chose to compare a daily dose of 400 mg/m2versus our standard dose of 200 mg/m2 (A200) in the first two treatment courses. (For patients who were not randomized between doses, a dose of 200 mg was specified.) Our previous AML11 trial compared a total of six courses versus three courses. Although there was a reduction in relapse risk, there was no OS benefit in the 6-course arm (Goldstone et al, 2001). It was conceivable that a fourth course of increased intensity might be advantageous compared with three additional courses of less intensity.

The four aims of the intensive arm of AML14 were therefore to compare a daunorubicin dose of 35 mg with 50 mg during induction: to assess the effect of combining the mdr modulator PSC-833 with daunorubicin; also, to evaluate an increased daily dose of Ara-C in induction, and finally to compare a total of three versus four courses of total treatment.

The intention to compare an intensive versus non-intensive treatment approach failed because only eight patients were randomized. In patients allocated to the non-intensive treatment approach, low dose Ara-C with or without retinoic acid would be compared with hydroxycarbamide/best supportive care with or without retinoic acid. These comparisons have already been reported in full elsewhere (Burnett et al, 2007), but the observation was that patients who received low dose Ara-C had a significantly better survival without any increased toxicity or supportive care requirements. However the benefit was limited to the 18% of patients who achieved complete remission and who did not have poor risk cytogenetics (Burnett et al, 2007).

Treatment details

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

At entry

The treatment plan is shown in Table I. Patients were randomized to be given two induction courses comprising daunorubicin by intravenous push on days 1, 2 and 3, Ara-C 12-hourly by intravenous 2-h infusion for 10 d in course 1 and for 8 d in course 2, and thioguanine orally 12-hourly for 10 d in course 1 and 8 d in course 2. Two dose variations were compared: patients were randomized to receive daunorubicin at a daily dose of either 35 or 50 mg/m2 and Ara-C at a daily dose of either 200 or 400 mg/m2. Patients who were allocated to 35 mg/m2 of daunorubicin were also randomized to receive or not PSC-833 with both courses, which was given as a loading dose of 2·0 mg/kg given over 2 h preceding the daunorubicin infusion, while simultaneously commencing an infusion of 10 mg/kg/24 h for 72 h. Course 2 was initiated 3–4 weeks from the end of course 1. On the completion of course 2, all patients who were in remission received a consolidation treatment course comprising mitoxantrone on days 1, 2 and 3 combined with Ara-C 12-hourly for 3 d. After recovery from course 3 patients were randomized to receive or not a fourth treatment course comprising idarubicin on days 1 and 3, Ara-C 12-hourly for 3 d and etoposide by a daily 1-h infusion for 3 d. Growth factor support was not routinely provided.

Table I.   Trial treatment schedule.
Course 1: DAT
 Daunorubicin 50 or 35 mg/m2 IV days 1, 2, 3 IV push
 Cytosine Arabinoside 100 or 200 mg/m2 IV push 12-hourly on day 1–10
 Thioguanine 100 mg/m2 oral 12-hourly days 1–10
 PSC-833 2·0 mg/kg IV loading dose over 2 h with simultaneous continuous infusion of 10 mg/kg/24 h for 72 h, both starting concurrently with the first daunorubicin 35 mg/m2 dose
Course 2: DAT
 Daunorubicin 50 or 35 mg/m2 IV days 1, 2, 3 IV push
 Cytosine Arabinoside 100 or 200 mg/m2 IV push 12 h on day 1–8
 Thioguanine 100 mg/m2 oral 12-hourly days 1–8
 PSC-833 2·0mg/kg IV loading dose over 2 h with simultaneous continuous infusion of 10 mg/kg/24 h for 72 h, both starting concurrently with the first daunorubicin 35 mg/m2 dose
Course 3: MIDAC
 Mitoxantrone 8 mg/m2 IV on days 1, 2 and 3
 Cytosine Arabinoside 0·5 g/m2 by 2-h infusion 12-hourly, days 1, 2 and 3
Course 4: ICE
 Idarubicin 10 mg/m2 by slow IV push on days 1 and 3 (two doses)
 Cytosine Arabinoside 100 mg/m2 by 2-h infusion 12-hourly, days 1, 2 and 3
 Etoposide 100 mg/m2 by 1-h infusion daily on days 1, 2 and 3

Inclusion and exclusion criteria

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

Patients were required to have any form of de novo or secondary AML, or MDS with more than 10% marrow blasts and to have given written consent. Patients were normally expected to be 60 years or older, but younger patients were permitted to enter if not thought fit to enter the contemporary trial for younger patients. Patients were excluded if they had had previous treatment for any form of leukaemia, were in blast transformation of chronic myeloid leukaemia, or had acute promyelocytic leukaemia or concurrent active malignancy. Patients with a history of heart disease or myocardial infarction within the last 6 months could not enter the daunorubicin dose or PSC-833 randomization. The trial was approved by the Wales Multicentre Research Ethics Committee and by the Ethical Committee of each participating institution and was conducted in accordance with the recommendations of the Declaration of Helsinki.

Methods

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

Definitions of endpoints

The protocol defined complete remission (CR) as a normocellular bone marrow aspirate containing <5% leukaemic blast cells and showing evidence of normal maturation of other marrow elements. The persistence of myelodysplastic features did not exclude the diagnosis of CR. Although not defined in the original protocol, to be defined in this report as CR patients had to have neutrophil recovery to 1·0 × 109/l and platelet recovery to 100 × 109/l; patients who achieved the bone marrow criteria of complete remission but failed to recover neutrophil or platelet counts were defined as CRi. Remission failures were classified by the investigating clinician as due either to induction death (ID), i.e. death within 30 d of the start of treatment related to treatment and/or hypoplasia, or as resistant disease (RD), i.e. related to the failure of therapy to eliminate the disease (including partial remissions with 5–15% blasts). Where the clinician’s evaluation was not available, deaths within 30 d of entry were classified as ID and deaths at more than 30 d as RD.

The following definitions are also used: overall survival (OS) was the time from randomization to death; for remitters, disease-free survival (DFS) was the time from CR/CRi to first event (either relapse or death in CR); and, for remitters, the relapse risk (RR) was the cumulative probability of relapse, ignoring (i.e. censoring at) death in first CR/CRi, and death in 1st CR (DCR) was the cumulative probability of dying in CR/CRi, ignoring relapse. For the consolidation question, DFS, RR and DCR were measured from date of the consolidation randomization.

Statistical methods

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

For time to event endpoints, Kaplan–Meier life-tables were constructed and were compared by means of the log-rank test. Surviving patients were censored at 1st January 2007 when follow-up was up-to-date for 98% of patients (the small number of patients lost to follow-up were censored at the date they were last known to be alive). All point estimates quoted are at 5 years. Median follow-up for surviving patients is 57 months (range 2–98 months).

Categorical endpoints (e.g. CR rates) were compared between arms by Mantel–Haenszel tests, giving rise to Peto odds ratios (OR) and confidence intervals (CI). Continuous variables (e.g. non-haematological toxicity and supportive care requirements) were analysed by parametric (t-test) or non-parametric (Wilcoxon rank sum, Kruskal–Wallis) tests as appropriate. Time to haematological recovery and days in hospital were analysed using the log-rank test. Statistical significance was set at P < 0·05.

Interactions between the randomized comparisons were investigated by stratified analyses, i.e. with each comparison adjusted for the others, using tests for heterogeneity over strata.

In addition to the overall analyses of the randomized comparisons, subgroup analyses were performed by the predefined stratification parameters (see above), although, because of small numbers, some groups were combined to give larger numbers and greater statistical reliability. Tests for heterogeneity of and/or trend in treatment effect between subgroups were performed. Because of the well-known dangers of subgroup analysis, all such analyses were interpreted cautiously. Analyses of prognostic factors, and those adjusting for multiple baseline parameters were performed using logistic or Cox regression as appropriate; models were built using forward selection with an entry probability of 0·05.

Odds ratios (OR) or hazard ratios (HR), with the 95% CI, are quoted for all main endpoints (CR, DFS, OS). In all cases, an OR or HR <1·0 indicated benefit for the investigational therapy (i.e. D35, D35 + PSC, A400, long consolidation). All P-values are two-tailed. All analyses were performed on the ‘intention to treat’ principle with all patients analysed in their allocated arms, irrespective of whether or not they actually received their allocated treatment.

Patient characteristics

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

The presenting features of the 1273 patients who entered the three induction randomizations and single consolidation randomization are shown in Table II, and the disposition of these patients is shown in Fig 1. A total of 177 patients did not enter the daunorubicin dose randomization; nine patients did not enter the Ara-C dose randomization. Six hundred and one patients were randomized between D50, D35, D35 + PSC; the 401 patients not allocated PSC were included in the comparison of the 896 patients randomized between D50 and D35. There were no differences in characteristics or outcomes between patients entering the randomized comparisons and those who elected one or other treatment. Cytogenetic information was available in 1005 (79%) patients and will be reported in detail elsewhere. Favourable karyotype was defined as t (8;21), or inv (16) irrespective of the presence of additional changes. Patients with monosomy 5, monosomy 7, del (5q), abnormalities of chromosome 3q or complex changes (≥5 unrelated abnormalities) were defined as adverse risk, while the remainder, including patients with normal karyotype, were regarded as intermediate risk. Cytogenetic analysis was performed in accredited regional laboratories and karyotypes centrally reviewed. Patients defined as having secondary AML had received previous chemotherapy/radiotherapy, had prior MDS or other antecedent haematological disorders in 26%, 52% and 33% of cases where a cause is known.

Table II.   Characteristics of AML14 patients by randomizations.
 TotalDaunorubicin doseDaunorubicin dose ± PSCAra-C doseConsolidation
D50D35D50D35D35 + PSCA200A400ShortLong
  1. AML, acute myeloid leukaemia; MDS, myelodysplastic syndrome; WBC, white blood cell count; FAB, French-American-British classification; RAEB, refractory anaemia with excess blasts; RAEB-T, RAEB in transformation; PGP, p-glycoprotein.

Number randomized1273446450201200200632632124126
Age group (years)
 <60331212655181534
 60–643701291306667651841854043
 65–695051791817675752512525248
 70–7426396953838381291292425
 75+1023032151517505156
Median (range)67 (44–88)67 (50–80)67 (44–88)66 (50–80)66 (56–88)66 (51–85)67 (44–82)67 (50–88)66 (58–77)66 (55–77)
Gender
 Female5011781657969892392575350
 Male7722682851221311113933757176
Type of disease
 de novo AML92032232913413513545745596101
 Secondary AML21173713534351021082115
 MDS14251503231307369710
WBC (×109/l)
 <10·06752352321071011063313426572
 10·0–49·93241191185559451671523034
 50·0–99·9136475120212864711310
 100·0–199·910433361617175251127
 200·0+341213324181643
Median (range)8·3 (0·1–460·0)8·0 (0·4–460·0)9·3 (0·3–386·0)8·4 (0·6–229·0)9·4 (0·3–317·0)8·6 (0·1–285·0)8·4 (0·1–460·0)8·0 (0·3–386·0)8·9 (0·4–386·0)7·5 (0·5–342·0)
Performance status
 07212582561151121133583577370
 14271481507069682152104542
 2752625111212363949
 3391116465192024
 411331124601
Cytogenetic group
 Favourable36919266181856
 Intermediate7402602541271241123773577573
 Adverse22982773933421141141715
 Unknown268951003337401231432730
FAB Type
 M08424261311193945117
 M126388953945391311282430
 M226695954737371301352923
 M34040200400
 M4165704627172980842021
 M59933291512194352912
 M64613108610291647
 M712654414800
 ALL4200013100
 Biphenotype4040101302
 RAEB331112867201332
 RAEB-T3212125105161632
 Other/unknown252921123549331361272120
PGP protein
 -ve/low112264017232764481616
 High1013525231928524878
PGP function
 Low119294118243168511518
 Intermediate56152191414243147
 High57231117915342346
image

Figure 1.  Disposition of subjects in AML14.

Download figure to PowerPoint

Compliance with treatment allocation

Information on compliance with allocated induction therapy was available for 98% of patients. Compliance was excellent for course 1, with 93% of patients (98% D50; 97% D35; 97% D35 + PSC; 95% A200, 93% A400) starting their allocated treatment. Fourteen patients (4 D50; 6 D35; 2 D35 + PSC; 6 A200; 8 A400) did not commence chemotherapy, while twelve patients (4 D50; 3 D35; 2 D35 + PSC; 8 A200; 4 A400) received other therapy. Non-compliant patients were included in the analysis.

Flow cytometry was used to measure p-glycoprotein and function in presentation bone marrow or peripheral blood blasts using MRK16 for protein and rhodamine 123 for function as described in detail elsewhere (Seedhouse et al, 2007).

Results

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

Overall results

Data on complete remission are available on 1271/1273 patients. The overall CR rate was 54%, with a further 8% of patients achieving a CRi, and with failure rates of 18% due to induction death and 20% due to resistant disease. For remitters, 5-year DFS was 13%, with DCR of 20% and RR of 84%. Of the 82 patients who died in first CR or CRi, 45 died within 6 months of remission usually from treatment-related causes (mainly infection). The 37 deaths beyond that point were due to various causes: infection (10), haemorrhage (two), cerebrovascular accident (CVA) (two), veno-occlusive disease (VOD) (one), other cancers (three) other causes (10) and unknown causes (nine). Five year OS from randomization was 12%.

Daunorubicin dose (50 mg/m2 vs. 35 mg/m2) randomization

There were no significant differences between the arms with respect to any of the major clinical endpoints (Fig 2).

image

Figure 2.  Results of D50 vs. D35 randomization.

Download figure to PowerPoint

Daunorubicin dose plus PSC-833 (50 mg/m2 vs. 35 mg/m2 vs. 35 mg/m2 + PSC) randomization

There was evidence of worse CR rate with D35 + PSC compared to daunorubicin alone (P = 0·07) arising from an excess of induction deaths (27% vs. 15%, P = 0·0003). Overall survival was also significantly worse (Fig 3). Interestingly, when a landmark analysis was performed, the adverse effect of PSC-833 was almost entirely restricted to the first few months following entry with a significant trend for diminishing effect with longer follow-up (see Fig 3).

image

Figure 3.  Results of D50 vs. D35 vs. D35 + PSC randomization.

Download figure to PowerPoint

Ara-C dose (200 mg/m2 vs. 400 mg/m2) randomization

There were no significant differences between the arms with respect to any of the major clinical endpoints (Fig 4).

image

Figure 4.  Results of A400 vs. A200 randomization.

Download figure to PowerPoint

Toxicity and supportive care

Courses 1 and 2 were evaluated for toxicity supportive care and haematopoietic recovery. There were no important differences between the treatments in non-haematological toxicity or in the number of days taken to recover neutrophil and platelet counts after courses 1 and 2. Resource usage and recovery time information is summarised in Tables III and IV. The median time from the start of course 1 to the start of course 2 was 41 d with no differences between treatment arms.

Table III.   Resource usage for induction randomization.
OutcomeD35 vs. D50P-valueD50 vs. D35 vs. D35 + PSCP-valueA400 vs. A200P-value
D35D50D35 +  PSCD35D50A400A200
  1. Using Wilcoxon rank-sum/Kruskal–Wallis test throughout.

Mean blood units
 Course 112·913·30·314·713·614·10·714·512·90·3
 Course 27·07·60·029·47·58·10·0068·57·30·0009
Mean platelets
 Course 1 17·018·10·918·118·719·80·920·315·80·02
 Course 28·58·70·0610·79·79·20·0211·48·20·0009
Mean antibiotic days
 Course 119·419·70·320·919·919·70·620·819·10·008
 Course 210·110·20·414·010·410·10·0111·410·40·04
Mean hospitalisation
 Course 134·434·50·633·735·634·40·1935·133·70·06
 Course 226·626·80·331·127·127·40·0229·526·0<0·0001
Table IV.   Recovery times for induction randomization.
OutcomeD35 vs. D50P-valueD50 vs. D35 vs. D35 + PSCP-valueA400 vs. A200P-value
D35D50D35 +  PSCD35D50A400A200
Neutrophil recovery to 1·0 × 109/l (median days)
 Course 119190·72019190·720190·8
 Course 218200·032218210·220180·8
Platelet recovery to 100 × 109/l (median days)
 Course 121220·82424210·822210·5
 Course 223270·053827270·0227270·9

Interactions between induction treatment arms

There was no significant evidence of any interactions between treatments. Likewise there was no evidence of interactions between treatment and baseline characteristics.

Consolidation randomization

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

The characteristics of the patients randomized to three versus four courses of therapy in total are shown in Table II. No advantage of the additional course was seen (Fig 5). The fourth course required a median of 22 d in hospital (mean 20·5), a median of 6 d on intravenous antibiotics, 4 units of platelets and 5 units of blood. This randomization continued the theme of more versus less treatment examined in a similar randomization, to three versus six courses of therapy, in the previous MRC AML11 trial (Goldstone et al, 2001). There were no interactions on any outcome between the consolidation randomization and either presenting characteristics or induction randomizations.

image

Figure 5.  Outcomes following consolidation.

Download figure to PowerPoint

Prognostic factors

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

On multivariate analysis, the factors that influenced outcome were cytogenetics, presenting white blood count, secondary disease and age. All were independently predictive for overall survival, but age was not predictive for CR/CRi, 8-week mortality or relapse (Table V).

Table V.   Results of Cox regression on outcome in AML14.
OS8 week mortalityCR/CRiRelapse
  1. OS, overall survival; CR, complete remission; CRi, CR without full neutrophil/platelet recovery; WBC, white blood cell count; HR, hazard ratio; OR, odds ratio.

Cytogenetic group (P < 0·0001): HR 1·99 (1·72–2·32) WBC (P < 0·0001): HR 1·003 (1·002–1·005) Secondary (P = 0·04) HR 1·12 (1·02–1·23) Age (P = 0·05) HR 1·014 (1·000–1·028)WBC (P = 0·0002) OR 1·005 (1·003–1·008) Cytogenetic group (P = 0·001) OR 1·70 (1·23–2·34)Cytogenetic group (P < 0·0001) OR 2·70 (2·03–3·60) WBC (P < 0·0001) OR 1·006 (1·004–1·009) Secondary (P = 0·02) OR 1·25 (1·03–1·51)Cytogenetic group (P < 0·0001) HR 1·71 (1·37–2·14) Secondary (P = 0·005) HR 1·23 (1·07–1·40) WBC (P = 0·009) HR 1·002 (1·001–1·004)

Pgp expression and function were determined in 235 patients. Patients with high levels of expression or function had a significantly lower remission rate due to disease resistance (Table VI). However it was not predictive of a higher relapse rate or inferior DFS in patients who achieved CR, but did predict for a poorer overall survival at 5 years (5% vs. 15%; P = 0·03). Of the 235 patients in whom Pgp was characterised by expression (n = 213) and/or function (n = 232), 154 were randomized to receive PSC-833 or not. The principal outcomes were not different between the arms and were not related to the level of Pgp expression or function (Fig 6) with the exception of an interaction between PSC-833 and the level of protein on CR, whereby the adverse effect of PSC-833 was much greater in patients with negative or low protein levels, with inconclusive evidence for patients with high protein levels. When the major prognostic factors described above were taken into account, Pgp expression or function was not an independent predictor of outcome. Pgp function and protein were highly correlated, with a Spearman correlation coefficient of 0·52, P < 0·0001.

Table VI.   Impact of PGP on outcome.
OutcomeProteinOR/HR, 95% CI, P-valueFunctionOR/HR, 95% CI P-value per group (logistic/Cox regression)
High-ve/lowHighIntermediateLow
  1. HR, hazard ratio; OR, odds ratio; 95% CI, 95% confidence interval; CR, complete remission; RD, resistant disease; ID, induction death; OS, overall survival.

No of patients1011125756119CRi
CRi %39713·62 (2·11–6·21) P < 0·00014061631·54 (1·12–2·11) P = 0·008
RD %37133·74 (1·99–7·01) P < 0·00013721151·80 (1·24–2·62) P = 0·002
ID %25171·60 (0·83–3·11) P = 0·162318221·00 (0·69–1·47) P = 1·0
30-day mortality (%)2118211421
8 week mortality (%)3122331827
OS at 5 years (%)5151·40 (1·04–1·87) P = 0·0355131·06 (0·90–1·25) P = 0·5
Relapse at 5 years (%)85820·93 (0·60–1·44) P = 0·78289810·98 (0·77–1·26) P = 0·9
image

Figure 6.  Interaction between PGP expression and PSC randomization.

Download figure to PowerPoint

The response category of CRi was achieved in 8% of patients. Reasons for definition were failure to recover platelets (n = 50), neutrophils (n = 7) or both (n = 19). Patients who were defined as CRi were less likely to receive course 3 or to be randomized to three versus four courses (9% of CRi patients vs. 35% of CR patients). The OS for CRi was inferior to patients defined as CR (10% vs. 20%: HR 1·56 (1·21–2·02). This was due to earlier relapse (1-year relapse 59% vs. 49%; 5-year relapse 88% vs. 83%; HR1·31 (1·00–1·70) P = 0·05) and an increased risk of death in CR (40% vs. 18% HR 2·87 (1·45–5·69) P = 0·006), and was not explained by any differences in baseline characteristics.

We included 142 patients who had 10–19·9% marrow blasts at diagnosis (high risk MDS). These patients did not differ in their characteristics from patients with >20% blasts, or indeed those with 20–30% blasts. The outcome for the MDS patients was no different from those with AML.

Discussion

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

Treatment of AML in older patients represents a number of important challenges. The first clinical decision is which patients should receive intensive treatment and who is unlikely to benefit from this approach and would be better off receiving a non-intensive option. While clinicians regularly make this choice, the factors that determine this decision are either not fully defined or not well understood. Retrospective data from collaborative groups or large single centre experience provide consistent prognostic information which identifies advanced age, poor risk cytogenetics, secondary disease, high white blood cell count at presentation and poor performance score as predictive of patients who are unlikely to benefit from an intensive approach, or even at risk of shortening of life (Stasi et al, 1996; Haferlach et al, 2004; Frohling et al, 2006; Kantarjian et al, 2006; Appelbaum et al, 2008; Farag et al, 2008). In order to further clarify this issue, risk scores providing a weighting to individual factors may more accurately characterise patients (Stasi et al, 1996; Sorror et al, 2005). The Sorror index (Sorror et al, 2005), which was based on the Charleson Index, has been useful in defining the risk of stem cell transplantation and has been successfully applied to older patients with AML who were given intensive chemotherapy (Giles et al, 2007). However it only identified a small proportion of cases that have a particularly poor outlook. We have previously provided a preliminary report of a risk score based on a summation of the coefficients in a Cox regression derived in the MRC AML11 trial (Wheatley et al, 2005), and this is reported in full elsewhere (Wheatley et al, 2009). This risk score is based on age, cytogenetics, performance status, white blood cell count, and diagnosis of de novo or secondary disease. If the AML11 patient population was divided into tertiles the score was highly prognostic, with survival at 3 years of 26%, 13% and 7% (P < 0·00001). Importantly the prognostic value of the index has been validated prospectively on this AML14 trial dataset, both in patients treated intensively (with 3-year survival of 26%, 17% and 9%, P < 0·0001) and non-intensively (with 1-year survival of 22%, 19% and 8%P < 0·0001). While such scoring systems have the potential to be useful in defining the outlook for individual patients, they do not necessarily give guidance as to whether one treatment approach is better than another because unrecognised factors may still play a role.

The response definition of CRi, i.e. marrow remission with failure of full recovery of neutrophils or platelets has become increasingly used. An examination of a large database has recently suggested that such patients have an inferior survival (Estey et al, 2007). In this study these patients had a significantly worse survival because of a higher risk of relapse and also more subsequent deaths without relapse. We have previously reported that in younger patients the blast response in the marrow taken around 23–25 d after chemotherapy was prognostic in that a residual blast percentage in excess of 15% independently predicted a higher risk of relapse in patients who subsequently entered remission (Wheatley et al, 1999). In this study, 65 patients had such a response and received course 2, of whom 26 achieved CR with a second course of the same treatment, although such patients had a significantly poorer survival than patients who achieved <5% blasts after the first course of treatment.

The AML14 trial was designed such that where the physician or patient was uncertain as to which approach to take, patients were to be randomized to intensive versus non-intensive treatment. In fact only eight of the 1485 patients recruited to the trial were randomized, which suggests that clinicians were confident about which approach to offer, although it is less clear what factors informed their choice. The issue of which treatment approach should be taken for each patient remains an important and unresolved issue in older patients. In order to extrapolate the information from clinical trials that may intentionally or unintentionally select patients, to a patient presenting in everyday practise, ways of accurately characterising patients are important. As new treatments are more frequently being tested in the older untreated population it is relevant to know which treatment would be regarded as the comparative standard treatment for that particular patient group.

In this trial the prognostic factors that emerged on multivariate analysis were those expected (age, cytogenetics, presenting white blood cell count, and secondary disease). Performance score was not identified as a prognostic factor, which is perhaps explained by the fact that only 10% of patients who were recruited had a performance score of >1. The observation that a greater number of older patients had high levels of Pgp is suggested as one of the reasons why older patients respond less well to treatment and has been shown to be prognostic for remission and overall survival. In this study Pgp was only characterised in 235 patients by either protein expression (n = 213) or efflux function (n = 232). About two-thirds of patients tested had high functional Pgp and about half had high levels of expression. While there may be debate about the definition of relevant levels of efflux or expression, we did not find that by adding Pgp status to the prognostic model that it was an independent prognostic factor for survival although the number of patients was limited. Molecular markers, such as FLT3 or nucleophosmin 1 (NPM1) mutations, were not assessed in this patient population, and although of clear prognostic value in younger patients their value in older patients is less clear.

A principal question in this trial was to evaluate targeting Pgp function using the modulator PSC-833. In our previous collaborative trial with the HOVON group it was established that the appropriate daily dose of daunorubicin to combine with PSC-833 was 35 mg/m2. In the subsequent randomized trial there was no difference between the daunorubicin 35 mg/m2 + PSC-833 and daunorubicin 50 mg/m2 arm, which has been considered as a negative result. However this conclusion pre-supposes that there was no difference between daunorubicin 35 and 50 mg/m2 dose levels. If the attenuated dose was inferior to the conventional dose, then the result seen could be considered positive in that the presence of PSC-833 compensated for the attenuated dose. The design of the induction intervention in the AML14 trial took this into account. Not only did this study fail to show benefit of adding PSC-833 to daunorubicin 35 mg/m2, but also that there was no difference between daunorubicin 35 and 50 mg/m2 either in response to induction treatment or on OS.

The PSC-833 randomization was closed prematurely because the drug became unavailable, but also because the Data Monitoring and Ethics Committee had concerns about an excess of induction deaths (27% vs. 15%). No single or exceptional cause was identified. A similar early death risk was seen in our previous high risk/relapse trial (AML-R) where ciclosporin was used as the modulator (Yin et al, 2001), and in the CALGB 9720 Trial in older patients in which both daunorubicin and etoposide were given in attenuated dose in combination with PSC-833 (Baer et al, 2002). However, even if early deaths were excluded from both arms there was no difference in efficacy. There was no evidence of benefit irrespective of the level of Pgp expression or function. In addition, resource usage and toxicity was increased in course 2.

The use of high dose Ara-C in induction has been examined in AML in younger patients without consistent evidence of improved OS. There have been not similar studies in older patients where the ability to deliver higher doses is limited. Given the dose response relationship in AML, it was reasonable to test the effect of doubling the standard dose of 200–400 mg/m2/d. However we found no evidence of benefit with respect to remission rate, induction death, 8 week mortality, disease free or overall survival, or harm with respect to toxicity, haematopoetic recovery or supportive care requirements. At this level of dose escalation no impact was observed, perhaps because no additional myelosuppression was noted.

Maintaining remission for those who achieve it is a challenge for AML patients of all ages. There is limited evidence for maintenance treatment with chemotherapy agents although trials of novel drugs, e.g. demethylation agents or the immunoconjugate gemtuzumab ozogamicin are ongoing. The optimal total number of courses is not known. In our AML11 trial we found no difference between a total of six versus three courses or the addition of interferon alpha for 12 months (Goldstone et al, 2001). In this present trial there was no benefit of adding a fourth course. Although there is no randomized evidence, there is a school of thought that no more than one post remission course is required. This question is currently being prospectively examined in our ongoing AML16 trial. It is clear that reduced intensity allogeneic transplantation has become a feasible option for patients of this age group although the encouraging preliminary results may only be relevant to a highly selected patient population, so it is important that this approach is prospectively assessed in the context of clinical trials.

Overall, the results of this trial bring into focus several of the problems in developing treatment in older patients with AML. Virtually no chemotherapeutic interventions, however well grounded in pre-clinical rationale, have improved survival in the last 20 years. Although remission rates have improved using the same chemotherapeutic agents, this can be explained by improved supportive care. There was optimism that progress could be made with growth factor support, which in general has not been realised. It is clear that new treatments and approaches to trial design are needed. Fortunately, several new agents are emerging, but there remains a significant challenge in evaluating them in large trials of traditional design. Promising agents need to be identified that are likely to bring significant improvement, and for economic reasons alone it is reasonable to require a clinically worthwhile treatment to make a noticeable rather than a marginal improvement. The emphasis on novel trial design, such as ‘pick a winner’ or adaptive randomization, should be greater in order to identify hopeful agents more rapidly, which can then be taken forward to more definitive evaluation.

Acknowledgements

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

The research costs of this trial were provided by the Leukaemia Research Fund of the United Kingdom. The PSC-833 was kindly supplied by Novartis. We are grateful to staff of the Birmingham Clinical Trials Unit for the data management for this trial, and to the following colleagues and research staff for participation in the trial:

Aberdeen Royal Infirmary: Dr D.J. Culligan, Dr J. Tighe; Addenbrooke’s Hospital: Dr J. Craig, Dr R.E. Marcus; Airedale General Hospital: Dr A.C. Cuthbert; Altnagelvin Area Hospital: Dr R.J.G. Cuthbert, Dr M.F. Ryan; Arrowe Park Hospital: Dr T.J. Deeble, Dr D.W. Galvani; Barnet General Hospital: Dr S.I. Berney, Dr D. Harvey, Dr A. Virchis; Belfast City Hospital: Dr R.J.G. Cuthbert, Dr F. Jones, Dr P. Kettle, Profr Mary Frances McMullin, Prof T.C.M. Morris; Birmingham Heartlands Hospital: Dr Chris Fegan, Dr R.J. Johnson, Dr D.W. Milligan, Dr G.E.D. Pratt; Borders General Hospital: Dr K. Gelly, Dr J. Tucker; Bradford Royal Infirmary: Dr L.J. Newton, Dr L A Parapia, Dr A.T. Williams; Bristol Haematology & Oncology Centre: Dr J.M. Bird, Dr R. Evely, Professor J.M. Hows, Prof D. Marks; Bristol Royal Infirmary: Dr G.L. Scott, Dr G.R. Standen; Cheltenham General Hospital: Dr E. Blundell, Dr S. Chown, Dr R.G. Dalton, Dr R. Lush, Dr J. Ropner; Chesterfield & North Derbyshire Royal Hospital: Dr R. Collin, Dr R. Stewart, Dr M. Wodzinski; Christie Hospital: Dr J. Cavet, Dr R. Chopra, Dr M. Dennis; Conquest Hospital: Dr J. Beard; Countess Of Chester Hospital: Dr J.V. Clough, Dr E. Rhodes; Crosshouse Hospital,Kilmarnock: Dr J.G. Erskine, Dr M. Mccoll, Dr P.D. Micallef-Eynaud; Derbyshire Royal Infirmary: Dr A. Mckernan, Dr D.C. Mitchell; Derriford Hospital: Dr A. Copplestone, Dr M. Hamon, Dr T.J. Nokes, Dr A. Prentice, Dr S. Rule; Dewsbury Hospitals: Dr M.R. Chapple; Diana, Princess of Wales Hospital: Dr K. Speed; Doncaster Royal Infirmary: Dr B. Paul; Dorset County Hospital: Dr A.H. Moosa; Dumfries & Galloway Royal Infirmary: Dr R.K.B. Dang; Ealing Hospital: Dr G. Abrahamson, Dr U.M. Hedge, Dr N. Philpott; Eastbourne District General Hospital: Dr P.A. Gover, Dr R.J. Grace; Falkirk and District Royal Infirmary: Dr A.D.J. Birch; George Eliot Hospital: Dr M. Narayanan; Glan Clwyd General Hospital: Dr D.R. Edwards, Dr D.I. Gozzard, Dr C. Hoyle; Glasgow Royal Infirmary: Dr G. Mcquaker, Dr A.N. Parker; Gloucestershire Royal Hospital: Dr S. Chown, Dr R. Lush; Good Hope Hospital: Dr M.S. Hamilton; Guy’s Hospital: Dr S.A. Schey; Hammersmith Hospital: Dr A. Rahemtulla; Harrogate District Hospital: Dr A.G. Bynoe; Hemel Hempstead General Hospital: Dr J. Harrison; Hereford County Hospital: Dr L.G. Robinson; Hillingdon Hospital: Dr R. Kaczmarski; Hinchingbrooke Hospital: Dr C. Hoggarth, Dr K. Rege; Hope Hospital: Dr J.B. Houghton; Huddersfield Royal Infirmary: Dr J.E. Braithwaite, Dr C. Carter; Hull Royal Infirmary: Dr S. Ali, Dr K. Patil, Dr M.L. Shields; Ipswich Hospital: Dr J.A. Ademokun, Dr I. Chalmers; James Paget Hospital: Dr T. Jeha, Dr S. Sadullah; Kent & Canterbury Hospital: Dr C.F.E. Pocock; Kettering General Hospital: Dr H. Kelsey, Dr M. Lyttleton, Dr I. Wilson-Morkeh; King George Hospital: Dr N. Akhtar, Dr I. Grant; King’s College Hospital (Denmark Hill): Professor G Mufti, Dr A. Pagliuca; Kingston Hospital NHS Trust: Dr M.R. Rowley, Dr H. Sykes; Leeds General Infirmary: Prof J.A. Child, Prof G.J. Morgan, Dr D.R. Norfolk, Dr G.M. Smith; Leicester Royal Infirmary: Dr C.S. Chapman, Dr A.E. Hunter, Dr B. Kennedy, Dr J.A. Snowden; Lincoln County Hospital: Dr A. Heppleston, Dr D.R. Prangnell, Dr C. Williams; Luton & Dunstable NHS Trust: Dr M. Sekhar; Manchester Royal Infirmary: Dr J. Burthem, Prof J.A. Liu Yin, Dr G.S. Lucas; Manor Hospital: Dr G.P. Galvin, Dr A. Jacob; Medway Maritime Hospital: Dr M. Aldouri; Milton Keynes General NHS Trust: Dr D.M. White; Monklands Hospital: Dr G. Cook, Dr J.A. Murphy, Dr I. Singer, Dr W.H. Watson; Mount Vernon Hospital: Dr K. Ardeshna; New Cross Hospital: Dr S. Basu, Dr A. Jacob; Ninewells Hospital: Dr D.T. Bowen, Dr P.G. Cachia, Dr D. Meiklejohn; North Hampshire Hospital: Dr L. Aston, Dr A. Milne; North Middlesex Hospital: Dr J. Luckit; North Staffordshire Hospital: Dr R.C. Chasty, Dr P.M. Chipping, Dr R.M. Ibbotson, Dr K.P. Schofield; Northampton General Hospital (Acute): Dr M.E. Haines; Northwick Park Hospital: Dr S. Allard, Dr T. Corbett, Dr N. Panoskaltsis, Dr C.D.L. Reid; Nottingham City Hospital: Dr J.L. Byrne, Dr A.P. Haynes, Dr P.A.E. Jones, Profr N.H. Russell; Oldchurch Hospital: Dr A. Brownell, Dr D. Lewis; Pembury Hospital: Dr R.F. Gale, Dr D. Gillett, Dr C.G. Taylor; Peterborough District Hospital: Dr M. Sivakumaran; Pilgrim Hospital: Dr S. Sobolewski, Dr V. Tringham; Pinderfields General Hospital: Dr M.C. Galvin, Prof P. Hillmen; Pontefract General Infirmary: Dr D. Wright; Poole General Hospital: Dr A.J. Bell, Dr F. Jack; Queen Alexandra Hospital: Dr T. Cranfield, Dr H. Dignum, Dr M. Ganczakowski; Queen Elizabeth Hospital (Kings Lynn): Dr P. Coates, Dr A.J. Keidan; Queen Elizabeth Hospital Birmingham: Dr J.A. Murray; Queen Mary’s Hospital: Dr S.J. Bowcock, Dr S. Rassam, Dr S. Ward; Raigmore Hospital: Dr P. Forsyth, Dr C. Lush, Dr W. Murray; Rotherham District General Hospital: Dr H.F. Barker, Dr P.C. Taylor; Royal Berkshire Hospital: Dr F. Brito-Babapulle, Dr H. Grech, Dr G. Morgenstern; Royal Bournemouth General Hospital: Prof T.J. Hamblin, Dr S. Killick, Dr D.G. Oscier; Royal Devon & Exeter Hospital (Wonford): Dr M.V. Joyner, Dr R. Lee, Dr M. Pocock, Dr C. Rudin; Royal Free Hospital: Dr M. Ethell, Dr P. Kottaridis, Dr A. Mehta, Dr M. Potter; Royal Gwent Hospital: Dr H.A. Jackson; Royal Hallamshire Hospital: Dr D.C. Rees, Prof J.T. Reilly, Dr J. Snowden, Dr D.A. Winfield; Royal Marsden Hospital: Dr C.E. Dearden, Dr M. Ethell; Royal Surrey County Hospital: Dr I. Douglas, Dr G. Robbins; Royal Victoria Hospital: Dr F. Jones, Prof Mary Frances McMullin; Russells Hall Hospital: Dr D. Bareford, Dr P. Harrison, Dr J. Neilson, Dr S.G.N. Richardson; Salisbury District Hospital: Dr J.O. Cullis, Dr H.F. Parry; Sandwell & West Birmingham Hospital: Dr D. Bareford, Dr J.G. Wright; Sandwell General Hospital: Dr Sunil Handa, Dr Y. Hasan, Dr P.J. Stableforth; Scunthorpe General Hospital: Dr R. Ezekwesili, Dr S. Jalihal; Singleton Hospital: Dr S. Al-Ismail; Southampton General Hospital: Dr A. Duncombe, Dr K. Orchard, Dr D. Richardson, Dr A. Smith; Southend Hospital: Dr A. Eden, Dr M.J. Mills; Southern General Hospital: Dr L.M. Manson, Dr A.E. Morrison; St George’s Hospital: Dr C.E. Dearden, Dr M. Scully; St Helier Hospital: Dr J. Behrens, Dr M. Clarke, Dr K. Rice; St James’s University Hospital: Dr D.L. Barnard, Dr Rod Johnson, Dr B.A. Mcverry; St Mary’s Hospital London: Dr S.H. Abdalla; St Richard’s Hospital: Dr Philip C. Bevan, Dr S. Janes, Dr P. Stross; St Thomas’ Hospital: Dr R. Carr; Staffordshire General Hospital: Dr A. Amos, Dr P. Revell; Stobhill NHS Trust: Dr M.T.J. Leach; Stoke Mandeville Hospital: Dr A. Watson; Thanet Healthcare NHS Trust: Dr K. Saied; The Alexandra Hospital: Dr S. Shafeek; The Ayr Hospital: Dr P. Vosylius; The Great Western Hospital, Reading: Dr N.E. Blesing, Dr A.G. Gray, Dr E.S. Green; The Princess Royal University Hospital: Dr C.F.M. De Lord, Dr A.K. Lakhani, Dr B. Vadher; The Royal Bolton Hospital: Dr M. Grey, Dr J. Jip; The Royal Liverpool University Hospital: Prof R.E. Clark; Torbay District General Hospital: Dr F. Booth, Dr P. Roberts, Dr N. Rymes, Dr S. Smith, Dr D. Turner; University College Hospital: Dr K. Ardeshna, Dr S. Devereux, Prof Anthony H. Goldstone, Prof A. Khwaja, Prof D.C. Linch, Dr A. Nathwani, Dr K.G. Patterson, Dr J.B. Porter, Dr K. Yong; University Hospital Aintree: Dr R. Dasgupta, Dr A. Olujohungbe, Dr W. Sadik, Dr B.E. Woodcock; University Hospital Lewisham: Dr M.L. Tillyer; University Hospital of Umea: Dr B. Backstrom, Dr B. Markevarn, Dr G. Sundstrom, Professor A. Wahlin; University Hospital of Wales: Prof A.K. Burnett, Dr Jonathan Kell, Dr C. Poynton, Dr C. Rowntree, Dr J.A. Whittaker; University of Ioannina: Prof K.L. Bourantas; Victoria Hospital: Dr S. Rogers; Victoria Infirmary, Glasgow: Dr R.A. Sharp, Dr P. Tansey; Walsgrave Hospital: Dr N. Jackson, Dr M.J. Strevens; Walton Hospital: Dr W. Sadik; Warwick Hospital: Dr S. Basu, Dr J. Mills, Dr Peter E. Rose; West Middlesex University Hospital: Dr C.E. Dearden, Dr R.G. Hughes, Dr M. Sekhar; Western General Hospital, Edinburgh: Dr J.M. Davies, Dr P. Ganly, Dr P.R.E. Johnson, Dr P.H. Roddie; Western Infirmary, Glasgow: Dr E.J. Fitzsimons, Dr N.P. Lucie, Dr R. Soutar; Whipps Cross University Hospital: Dr C.C. Anderson, Dr C. DeSilva; Whiston Hospital: Dr G. Satchi, Dr J.A. Tappin; William Harvey Hospital (Ashford): Dr R.F. Gale; Wishaw General Hospital: Dr T.L. Allan; Worcestershire Royal Hospital: Dr R. Stockley; Worthing Hospital: Dr A.M. O’Driscoll, Dr C.L. Rist; Wycombe General Hospital: Dr R. Aitchison, Dr S. Kelly, Dr J. Pattinson; York District General Hospital: Dr L.R. Bond, Dr M.R. Howard; Ysbyty Gwynedd: Dr M. Gilleece, Dr H. Parry, Dr James Seale.

Statement of authors’ contribution

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References

AKB, DM, AG, AGP, KW designed the study; AKB, DM, AHG, AGP, MFM, MD, ES, MP, NR, RKH, KW performed the research; RKH analysed data; AKB and RKH wrote the manuscript which was edited by all authors.

References

  1. Top of page
  2. Summary
  3. Treatment details
  4. Inclusion and exclusion criteria
  5. Methods
  6. Statistical methods
  7. Patient characteristics
  8. Results
  9. Consolidation randomization
  10. Prognostic factors
  11. Discussion
  12. Acknowledgements
  13. Statement of authors’ contribution
  14. References
  • Appelbaum, F.R., Gundacker, H., Head, D.R., Slovak, M.L., Willman, C.L., Godwin, J.E., Anderson, J.E. & Petersdorf, S.H. (2008) Age and acute myeloid leukemia. Blood, 107, 34813485.
  • Baer, M.R., George, S.L., Dodge, R.K., O’Loughlin, K.L., Minderman, H., Caligiuri, M.A., Anastasi, J., Powell, B.L., Kolitz, J.E., Schiffer, C.A., Bloomfield, C.D. & Larson, R.A. (2002) Phase 3 study of the multidrug resistance modulator PSC-833 in previously untreated patients 60 years of age and older with acute myeloid leukemia: Cancer and Leukemia Group B Study 9720. Blood, 100, 12241232.
  • Bishop, J.F., Matthews, J.P., Young, G.A., Szer, J., Gillett, A., Joshua, D., Bradstock, K., Enno, A., Wolf, M.M., Fox, R., Cobcroft, R., Herrmann, R., Van Der Weyden, M., Lowenthal, R.M., Page, F., Garson, M. & Juneja, S. (1996) A randomized study of high-dose cytarabine in induction in acute myeloid leukemia. Blood, 87, 17101717.
  • Burnett, A.K. & Mohite, U. (2006) Treatment of older patients with acute myeloid leukemia – new agents. Seminars in Hematology, 43, 96106.
  • Burnett, A.K., Milligan, D., Prentice, A.G., Goldstone, A.H., McMullin, M.F. & Wheatley, K. (2007) A comparison of low-dose cytarabine and hydroxyurea with or without all-trans retinoic acid for acute myeloid leukemia and high-risk myelodysplastic syndrome in patients not considered fit for intensive treatment. Cancer, 109, 11141124.
  • Campos, L., Guyotat, D., Archimbaud, E., Calmard-Oriol, P., Tsuruo, T., Troncy, J., Treille, D. & Fiere, D. (1992) Clinical significance of multidrug resistance P-glycoprotein expression in acute non-lymphoblastic leukemia cells at diagnosis. Blood, 79, 473476.
  • Estey, E., Sun, Z., Rowe, J., Faderl, S., Cassileth, P., Sartiano, G., Tartaglia, A., Garcia-Manero, G., Cripe, L., Bennett, J., Kantarjian, H. & Tallman, M. (2007) A3,239 - patient combined Eastern Cooperative Oncology Group (ECOG), MD Anderson Cancer Center (MDA) Analysis of the effect of CR vs responses < CR on Long-Term survival in newly diagnosed AML treated with Ara-C-containing regimens: Implications for targeted drug development. Blood, 110, 94a.
  • Farag, S.S., Archer, K.J., Mrozek, K., Ruppert, A.S., Carroll, A.J., Vardiman, J.W., Pettenati, M.J., Baer, M.R., Qumsiyeh, M., Koduru, P.R., Ning, Y., Mayer, R.J., Stone, R.M., Larson, R.A. & Bloomfield, C.D. (2008) Pretreatment cytogenetics add to other prognostic factors predicting complete remission and long-term outcome in patients 60 years of age or older with acute myeloid leukemia: results from Cancer the Leukemia Group B 8461. Blood, 108, 6373.
  • Filipits, M., Stranzl, T., Pohl, G.H., Heinzl, H., Jager, U., Geissler, K., Fonatsch, C., Haas, O.A., Lechner, K. & Pirker, R. (2000) Drug resistance factors in acute myeloid leukemia: a comparative analysis. Leukemia, 14, 6876.
  • Frohling, S., Schlenk, R.F., Kayser, S., Ruppert, A.S., Carroll, A.J., Vardiman, J.W., Pettenati, M.J., Baer, M.R., Qumsiyeh, M., Koduru, P.R., Ning, Y., Mayer, R.J., Stone, R.M., Larson, R.A. & Bloomfield, C.D. (2006) Cytogenetics and age are major determinants of outcome in intensively treated acute myeloid leukemia patients older than 60 years: results from AMLSG trial AML HD98-B. Blood, 108, 32803288.
  • Giles, F.J., Borthakur, G., Ravandi, F., Faderl, S., Verstovsek, S., Thomas, D., Wierda, W., Ferrajoli, A., Kornblau, S., Pierce, S., Albitar, M., Cortes, J. & Kantarjian, H. (2007) The haematopoietic cell transplantation comorbidity index score is predictive of early death and survival in patients over 60 years of age receiving induction therapy for acute myeloid leukaemia. British Journal of Haematology, 136, 624627.
  • Goldstone, A.H., Burnett, A.K., Wheatley, K., Smith, A.G., Hutchinson, R.M. & Clark, R.E. (2001) Attempts to improve treatment outcomes in acute myeloid leukaemia (AML) in older patients: the results of the United Kingdom Medical Research Council AML11 trial. Blood, 98, 13021311.
  • Haferlach, T., Kern, W., Schoch, C., Schnittger, S., Sauerland, M.C., Heinecke, A., Buchner, T. & Hiddemann, W. (2004) A new prognostic score for patients with acute myeloid leukemia based on cytogenetics and early blast clearance in trials of the German AML Cooperative Group. Haematologica, 89, 389390.
  • Van Den Heuvel-Eibrink, M.M., Van Der Holt, B., Te Boekhorst, P.A., Pieters, R., Schoester, M., Lowenberg, B. & Sonneveld, P. (1997) MDR 1 expression is an independent prognostic factor for response and survival in de novo acute myeloid leukaemia. British Journal of Haematology, 99, 7683.
  • Holt, B. van der, Lowenberg, B., Burnett, A.K., Knauf, W.U., Shepherd, J., Piccaluga, P.P., Ossenkoppele, G.J., Verhoef, G.E., Ferrant, A., Crump, M., Selleslag, D., Theobald, M., Fey, M.F., Vellenga, E., Dugan, M. & Sonneveld, P. (2005) The value of the MDR1 reversal agent PSC-833 in addition to daunorubicin and cytarabine in the treatment of elderly patients with previously untreated acute myeloid leukemia (AML), in relation to MDR1 status at diagnosis. Blood, 106, 26462654.
  • Hunault, M., Zhou, D., Delmer, A., Ramond, S., Viguie, F., Cadiou, M., Perrot, J.Y., Levy, V., Rio, B., Cymbalista, F., Zittoun, R. & Marie, J.P. (1997) Multidrug resistance gene expression in acute myeloid leukemia: major prognosis significance for in vivo drug resistance to induction treatment. Annals of Hematology, 74, 6571.
  • Kantarjian, H., O’Brien, S., Cortes, J., Giles, F., Faderl, S., Jabbour, E., Garcia-Manero, G., Wierda, W., Pierce, S., Shan, J. & Estey, E. (2006) Results of intensive chemotherapy in 998 patients age 65 years or older with acute myeloid leukemia or high-risk myelodysplastic syndrome. Cancer, 106, 10901098.
  • Leith, C.P., Kopecky, K.J., Chen, I.M., Eijdems, L., Slovak, M.L., McConnell, T.S., Head, D.R., Weick, J., Grever, M.R., Appelbaum, F.R. & Willman, C.L. (1999) Frequency and clinical significance of the expression of the multidrug resistance proteins MDR1/P-glycoprotein, MRP1 and LRP in acute myeloid leukemia: a Southwest Oncology Group Study. Blood, 94, 10861099.
  • List, A.F., Kopecky, K.J., Willman, C., Head, D.R., Persons, D.L., Slovak, M.L., Dorr, R., Karanes, C., Hynes, H.E., Doroshow, J.H., Shurafa, M. & Appelbaum, F.R. (2001) Benefit of cyclosporin modulation of drug resistance in patients with poor-risk acute myeloid leukemia: a Southwest Oncology Group Study. Clinical Observations, Interventions and Therapeutic Trials, 98, 32123220.
  • Lowenberg, B., Zittoun, R., Kerkhofs, H., Jehn, U., Abels, J., Debusscher, L., Cauchie, C., Peetermans, M., Solbu, G. & Suciu, S. (1989) On the value of intensive remission-induction chemotherapy in elderly patients of 65 +  years with acute myeloid leukemia: a randomized phase III study of the European Organization for Research and Treatment of Cancer Leukemia Group. Journal of Clinical Oncology, 7, 12681274.
  • Mayer, R.J., Davis, R.B., Schiffer, C.A., Berg, D.T., Powell, B.L., Schulman, P., Omura, G.A., Moore, J.O., McIntyre, O.R. & Frei, E. (1994) Intensive postremission chemotherapy in adults with acute myeloid leukemia. Cancer and Leukemia Group B. New England Journal of Medicine, 331, 896903.
  • Menzin, J., Lang, K., Earle, C., Kerney, D. & Mallick, R. (2002) The outcomes and costs of Acute Myeloid Leukemia among the elderly. Archives of Internal Medicine, 162, 15971603.
  • Poeta, G. Del, Stasi, R., Venditti, A., Cox, M.C., Bruno, A., Buccisano, F., Masi, M., Tribalto, M., Amadori, S. & Papa, G. (1996) Clinical relevance of P-glycoprotein expression in de novo acute myeloid leukemia. Blood, 87, 19972004.
  • Seedhouse, C.H., Grundy, M., White, P., Li, Y., Fisher, J., Yakunina, D., Moorman, A.V., Hoy, T., Russell, N., Burnett, A. & Pallis, M. (2007) Sequential influences of leukemia-specific and genetic factors on p-glycoprotein expression in blasts from 817 patients entered into the National Cancer Research Network acute myeloid leukemia 14 and 15 trials. Clinical Cancer Research, 13, 70597066.
  • Sonneveld, P., Burnett, A.K., Vossebeld, P., Benn-Am, M., Rosenkranz, G., Pfister, C., Verhoef, G., Dekker, A., Ossenkoppele, G., Ferrant, C., Yin, L., Gratwohl, A., Kovacsovics, T., Vellenga, E., Capdeville, R. & Lowenberg, B. (2000) Dose-finding study of valspodar (PSC 833) with daunorubicin and cytarabine to reverse multidrug resistance in elderly patients with previously untreated acute myeloid leukemia. The Hematology Journal, 1, 411421.
  • Sorror, M.L., Maris, M.B., Storb, R., Baron, F., Sandmaier, B.M., Maloney, D.G. & Storer, B. (2005) Hematopoietic cell transplantation (HCT)- specific comorbidity index: a new tool for risk assessment before allogeneic HCT. Blood, 106, 29122919.
  • Stasi, R., Venditti, A., Del Poeta, G., Aronica, G., Dentamaro, T., Cecconi, M., Stipla, E., Scimo, M.T., Masi, M. & Amadori, S. (1996) Intensive treatment of patients age 60 years and older with de novo acute myeloid leukemia. Cancer, 77, 24762488.
  • Taylor, P.R.A., Reid, M.M., Stark, A.N., Brown, N., Hamilton, P.J. & Proctor, J. (1995) De novo acute myeloid leukemia in patients over 55 years old: a population-based study of incidence, treatment and outcome. Leukemia, 9, 231237.
  • Tilly, H., Castaigne, S., Bordessoule, D., Casassus, P., Le Prise, P.Y., Tertian, G., Desablens, B., Henry-Amar, M. & Degos, L. (1990) Low-dose cytarabine versus intensive chemotherapy in the treatment of acute non-lymphocytic leukemia in the elderly. Journal of Clinical Oncology, 8, 272279.
  • Weick, J.K., Kopecky, K.J., Appelbaum, F.R., Head, D.R., Kingsbury, L.L., Balcerzak, S.P., Bickers, J.N., Hynes, H.E., Welborn, J.L., Simon, S.R. & Grever, M. (1996) A randomized investigation of high-dose versus standard dose Cytosine Arabinoside with Daunorubicin in patients with previously untreated acute myeloid leukemia: A Southwest Oncology Group Study. Blood, 88, 28412851.
  • Wheatley, K., Burnett, A.K., Goldstone, A.H., Gray, R.G., Hann, I.M., Harrison, C.J., Rees, J.K., Stevens, R.F. & Walker, H. (1999) A simple, robust, validated and highly predictive index for the determination of risk-directed therapy in acute myeloid leukaemia derived from the MRC AML 10 trial. United Kingdom Medical Research Council’s Adult and Childhood Leukaemia Working Parties. British Journal of Haematology, 107, 6979.
  • Wheatley, K., Brookes, C.L., Hills, R.K., Goldstone, A.H., Milligan, D.W., Prentice, A.G., Moorman, A.V. & Burnett, A.K. (2005) Prognostic factors in older AML patients receiving intensive and non-intensive therapy: analysis of the UK AML11 and AML14 Trials. Blood, 106, 199a.
  • Wheatley, K., Brookes, C.L., Howman, A.J., Goldstone, A.H., Milligan, D.W., Prentice, A.G., Moorman, A. & Burnett, A.K. (2009) Prognostic factor analysis of the survival of elderly patients with AML in the MRC AML11 and LRF AML14 Trials. British Journal of Haematology, (in press).
  • Yin, J.A.L., Wheatley, K., Rees, J.K. & Burnett, A.K. (2001) Comparison of ‘sequential’ versus ‘standard’ chemotherapy as re-induction treatment, with or without cyclosporin, in refractory/relapsed acute myeloid leukaemia (AML): results of the UK Medical Research Council AML-R Trial. British Journal of Haematology, 113, 713726.