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Effect of abiraterone acetate treatment on the quality of life of patients with metastatic castration-resistant prostate cancer after failure of docetaxel chemotherapy

European Journal of Cancer, 17, 49, pages 3648 - 3657

Abstract

Background

In a recent randomised, double-blind, phase III clinical trial among 1195 patients with metastatic castration-resistant prostate cancer (mCRPC) who had failed docetaxel chemotherapy, abiraterone acetate was shown to significantly prolong overall survival compared with prednisone alone. Here we report on the impact of abiraterone therapy on the health-related quality of life (HRQoL) observed during this trial, assessed using the validated Functional Assessment of Cancer Therapy-Prostate (FACT-P) questionnaire.

Methods

All analyses were conducted using prespecified criteria for clinically meaningful improvement and deterioration in FACT-P total score as well as subscale scores; all respective thresholds were defined using an accepted methodology. Improvement was assessed only in patients with clinically significant functional status impairment at baseline.

Results

Significant improvements in the FACT-P total score were observed in 48% of patients receiving abiraterone versus 32% of patients receiving prednisone (p<0.0001). Also, the median time to deterioration in FACT-P total score was longer (p<0.0001) in patients receiving abiraterone (59.9weeks versus 36.1weeks). Similar differences were observed in all FACT-P subscales, with the exception of the social/family well-being domain. Median time to improvement in the physical well-being domain and the trial outcome index was significantly shorter (p<0.01) with abiraterone when compared with the prednisone arm.

Conclusions

The previously demonstrated survival benefit for abiraterone is accompanied by improvements in patient-reported HRQoL and a significant delay in HRQoL deterioration when compared with prednisone.

Keywords: Abiraterone, Prostate, Health, Quality of life, Outcome assessment.

1. Introduction

Along with prolongation of survival, improved health-related quality of life (HRQoL) is an important therapeutic objective for patients with metastatic castration-resistant prostate cancer (mCRPC) [1], [2], and [3], due to concerns about the wide, varied spectrum of disease-related symptoms and the side-effects of therapy [4]. Assessment of HRQoL is increasingly incorporated into prospective studies of therapies for advanced prostate cancer [1], [3], [5], [6], [7], [8], and [9] and patient-based questionnaires are considered more reliable and informative than interpretations assigned by study investigators [5]. Many novel therapies for mCRPC are available, namely abiraterone, cabazitaxel, enzalutamide and sipuleucel-T, but little is known about their impact on HRQoL.

Abiraterone acetate is a potent androgen biosynthesis inhibitor [10]. A recent randomised double-blind clinical trial of 1195 men with mCRPC progressing after docetaxel chemotherapy showed that abiraterone significantly prolonged overall survival compared with prednisone [11]. We present patient-reported data from the same trial, collected using the validated Functional Assessment of Cancer Therapy-Prostate (FACT-P) questionnaire, in order to determine whether abiraterone improved HRQoL. Outcomes were reported using changes in the FACT-P and its subscales that have been established as clinically meaningful in this patient population.

2. Patients and methods

2.1. Trial design

Study COU-AA-301 was an international, phase III, randomised, double-blind, placebo-controlled trial for patients with mCRPC post-docetaxel (clinicaltrials.gov: NCT00638690). Patients received oral abiraterone acetate (1g daily) plus prednisone (5mg twice daily) or placebo plus prednisone (5mg twice daily), both regimens taken continuously on 4-weekly cycles. This study was approved by the review boards at all participating institutions and was conducted according to the principles set forth in the Declaration of Helsinki and the Good Clinical Practice guidelines of the International Conference on Harmonisation. All patients provided written informed consent to participate. The study design and primary and secondary end-point results are published elsewhere [11] and [12].

2.2. Assessments

HRQoL was assessed at baseline and on day 1 of cycles 1, 4, 7, 10 and every six cycles thereafter until the end of study treatment, using the FACT-P questionnaire. Validated translations of this instrument [13] were provided to all patients in non-English-speaking countries. The FACT-P is a standard questionnaire that is validated in the mCRPC population [4], [14], [15], and [16]. It consists of a general HRQoL assessment (the FACT-G measure, which comprises four multi-item subscales) and a multi-item prostate cancer–specific subscale (Table 1). For all subscales, higher scores indicate better outcomes. The following analyses were conducted for each FACT-P sub- and composite scale:

  • Improvement in FACT-P scores: Only patients who had impaired HRQoL were considered for the improvement analyses. To be eligible, patients needed to have a baseline score at or below the threshold listed for the respective (sub)scale (Table 1). Patients with good baseline functional status were excluded since their baseline scores were too close to the maximum possible score to detect a measured benefit. Eligible patients with at least one follow-up visit at which the score had increased (compared with baseline) by the amount stated in Table 1 were considered to have had improvement in the respective (sub)scale.
  • Deterioration in FACT-P scores: All patients started with baseline scores that were at least as far from the minimum (worst) possible score (zero) as the predefined meaningful improvement (Table 1, rightmost two columns), thereby allowing each patient to report a clinically meaningful deterioration. Patients with at least one follow-up visit at which the score had decreased (compared with baseline) by the amount stated in Table 1 were considered to have had deterioration in the respective (sub)scale.
  • Time to improvement/deterioration: The time period from randomisation to the first observed occurrence of improvement/deterioration.

Table 1 List of the eight outcome scores derived from the FACT-P questionnaire, along with definitions of eligibility criteria and outcomes used in the respective analyses of this study.

FACT-P scale/subscale No. of items Possible score range Meaning of higher score Baseline score eligibility threshold for improvement analyses Minimum increase from baseline required to meet definition of improvement Minimum decrease from baseline required to meet definition of deterioration
FACT-P total scalea 39 0–156 Better overall HRQoL ⩽122 10 10
Physical well-being 7 0–28 Better physical well-being ⩽25 3 3
Social/family well-being 7 0–28 Better social/family well-being ⩽21 3 3
Emotional well-being 6 0–24 Better emotional well-being ⩽13 3 3
Functional well-being 7 0–28 Better functional well-being ⩽19 3 3
FACT-G scaleb 27 0–108 Better general HRQoL ⩽88 9 9
Prostate cancer subscale 12 0–48 Better prostate cancer-specific HRQoL ⩽34 3 3
Trial outcome indexc 26 0–104 Better HRQoL ⩽79 9 9

a Composite of the score on the FACT-G scale+the score on the prostate cancer subscale.

b Composite of the scores on physical well-being+social/family well-being+emotional well-being+functional well-being.

c Composite of the scores on physical well-being+functional well-being+prostate cancer subscale.

FACT-P, Functional Assessment of Cancer Therapy-Prostate; FACT-G, FACT-General; HRQoL, health-related quality of life.

The thresholds for eligibility for the improvement analyses were determined by subtracting half the standard deviation from the mean scores of a validity sample of 96 prostate cancer patients of all stages, reported in the original validation analysis of the FACT-P questionnaire [15]. The thresholds for clinically meaningful improvement or deterioration in FACT-P total score and subscale scores (i.e. a 0.5 effect size) were similar to those derived from analyses in prostate cancer patients reported in prior publications and are summarised in Table 1[14]. No imputation of missing assessments was conducted for this analysis.

2.3. Statistical analysis

The proportion of expected (i.e. not excluding patients who died or dropped out of the study) versus completed FACT-P assessments collected at each study visit was evaluated to determine compliance. The proportions of patients with improvement/deterioration in FACT-P were compared between treatment groups using the chi-square test.

Time to improvement and time to deterioration were estimated using the Kaplan–Meier method. Differences between treatment groups were compared using a log-rank test stratified by Eastern Cooperative Oncology Group (ECOG) performance status, pain score, number of prior chemotherapy regimens and type of progression. Corresponding hazard ratios (HRs) were estimated using a Cox proportional hazard model stratified by the same parameters. If median values could not be computed because >50% of patients improved/deteriorated during the observed follow-up time, the 25th percentiles were computed instead.

Since missing data challenge the accurate evaluation of HRQoL outcomes [17], it is generally recommended that sensitivity analyses be performed on the results from planned analyses [18]. Suitable sensitivity analyses can help detect and/or overcome bias introduced by non-random missing data. Thus, mean FACT-P total and subscale scores over the duration of the study were compared between the treatment groups using two sets of mathematical models: repeated-measures mixed-effects models and joint mixed-effects and log time-to-dropout models, based on previously described methods [19] and [20]. This comparison ensured that observed differences in FACT-P total and subscale scores between treatment groups were not skewed due to selection bias of healthier patients in any study group because of missing data and/or discontinuation. All models were developed using SAS Version 9.3 (SAS Institute Inc., Cary, NC, United States of America (USA)).

Repeated-measures mixed-effects models estimated mean scores at each treatment cycle as a function of treatment, visit number, treatment versus visit interaction and baseline prostate-specific antigen. These models simultaneously estimated the intercept and slope of the patient-reported outcome (PRO) curve and treated missing data as ‘missing at random.’ A t test was used to compare estimated score changes (compared with baseline) at each time point between treatment groups. Joint mixed-effects and log time-to-dropout models estimated FACT-P score profiles over time for each treatment arm; since FACT-P scores were not linear over the course of the study, these models assumed the rate of score change to vary at weeks 16, 28 and 40. These models simultaneously estimated the intercept, slope and time to dropout and treated missing FACT-P data as ‘missing not at random.’ The areas under the curve (AUC) for each treatment arm were calculated using the regression parameter estimates based on the trapezoidal rule with preset definitions and compared using the t test.

3. Results

3.1. Patient characteristics

A total of 797 patients were randomised to abiraterone and 398 to prednisone. The median treatment duration was 7.4 and 3.6months, respectively [11]. Median duration of follow-up for the overall study population was 20.2months [11]. Patient compliance with the FACT-P questionnaire was high. The cumulative amount of available data, excluding deaths and study dropouts, in the updated analysis was 91.3% at cycle 28 (at that time point, only 326 (9%) of a total of 3752 possible assessments were not collected). The baseline FACT-P scores (for total scale and all subscales) were similar between treatment groups (Supplementary Table 1). The number of patients who contributed to the analyses of improvement in the overall scale and each subscale is shown in Table 2.

Table 2 Symptomatic improvement in the FACT-P total scale and all subscales.

FACT-P scale/subscale Abiraterone–prednisone, n (%) Prednisone alone, n (%) p Value
FACT-P total scalea 271/563 (48.1) 87/273 (31.9) <0.0001
Physical well-being 287/616 (46.6) 84/295 (28.5) <0.0001
Social/family well-being 168/292 (57.5) 64/130 (49.2) 0.1134
Emotional well-being 82/147 (55.8) 24/62 (38.7) 0.0241
Functional well-being 217/487 (44.6) 84/249 (33.7) 0.0047
FACT-G scaleb 239/568 (42.1) 79/283 (27.9) 0.0001
Prostate cancer subscale 325/554 (58.7) 101/255 (39.6) <0.0001
Trial outcome indexc 260/557 (46.7) 69/271 (25.5) <0.0001

a Composite of the score on the FACT-G scale+the score on the prostate cancer subscale.

b Composite of the scores on physical well-being+social/family well-being+emotional well-being+functional well-being.

c Composite of the scores on physical well-being+functional well-being+prostate cancer subscale.

FACT-P, Functional Assessment of Cancer Therapy-Prostate; FACT-G, FACT-General; Shown for each analysis is the proportion, and number of patients with improvement out of the number of patients eligible for that particular analysis.

3.2. FACT-P outcomes

Abiraterone resulted in significantly better FACT-P outcomes than prednisone, with the exception of the Social/Family Well-Being (SFWB) subscale, in which no significant difference was observed (Table 2). A post hoc sensitivity analysis of improvement including the total ITT population yielded similar results: all differences remained significant (p<0.001), and abiraterone also showed a significant (p=0.041) advantage in the SFWB subscale (data not shown). With the exception of the SFWB subscale, the association between treatment and improvement remained significant (p<0.001) after adjusting for baseline score, based on results from the Cochran–Mantel–Haenszel test for general association (data not shown).

With abiraterone, we observed a significantly shorter median time to improvement in the FACT-P physical well-being (PWB) subscale and trial outcome index (TOI) composite scales (Table 3 and Fig. 1); the Kaplan–Meier curves suggest a modest effect. Abiraterone also significantly delayed deterioration in FACT-P total scores and in all subscales (Table 3 and Fig. 2), except the SFWB subscale.

Table 3 Time to improvement and deterioration in FACT-P total scale, subscales and composite scales.

  Abiraterone–prednisone Prednisone alone p-Value by log-rank test Cox analysis hazard ratio (95% CI)a
Median time to FACT-P improvement, days
FACT-P total scaleb 114 170 0.1085 1.213 (0.950–1.550)
Physical well-being 168 254 0.0088 1.377 (1.075–1.763)
Social/family well-being 148 114 0.2419 0.839 (0.620–1.135)
Emotional well-being 86 88 0.6605 1.111 (0.684–1.804)
Functional well-being 168 169 0.7282 1.045 (0.807–1.354)
FACT-G scalec 171 254 0.0784 1.254 (0.969–1.623)
Prostate cancer subscale 86 87 0.2311 1.141 (0.909–1.434)
Trial outcome indexd 114 325 0.0006 1.573 (1.203–2.055)
Median time to FACT-P deterioration, days
FACT-P total scaleb 419 253 <0.0001 0.607 (0.495–0.743)
Physical well-being 340 240 <0.0001 0.657 (0.539–0.801)
Social/family well-being 510 504 0.2710 0.875 (0.689–1.112)
Emotional well-being 424 226 <0.0001 0.618 (0.500–0.764)
Functional well-being 338 169 <0.0001 0.625 (0.513–0.761)
FACT-G scalec 478 274 <0.0001 0.634 (0.511–0.788)
Prostate cancer subscale 282 142 <0.0001 0.611 (0.507–0.735)
Trial outcome indexd 422 253 <0.0001 0.615 (0.499–0.756)

a Hazard ratios higher than 1 favour abiraterone–prednisone for time to improvement, while hazard ratios smaller than 1 favour abiraterone-prednisone for time to deterioration.

b Composite of the score on the FACT-G scale+the score on the prostate cancer subscale.

c Composite of the scores on physical well-being+social/family well-being+emotional well-being+functional well-being.

d Composite of the scores on physical well-being+functional well-being+prostate cancer subscale.

CI, Confidence interval; FACT-P, Functional Assessment of Cancer Therapy-Prostate; FACT-G, FACT-General.

gr1

Fig. 1 Time to symptomatic improvement in FACT-P total score (A) and prostate cancer subscale (PCS) score (B).

gr2

Fig. 2 Time to symptomatic degradation in FACT-P total score (A) and prostate cancer subscale (PCS) score (B).

All results obtained at the preplanned interim analysis (see Supplementary material) were similar to those from the final dataset.

3.3. FACT-P data modelling

The model estimates for FACT-P data also indicated a favourable FACT-P profile of abiraterone compared with prednisone. The repeated-measures mixed-effects model showed that the change in FACT-P total score from baseline was consistently better with abiraterone than prednisone (Fig. 3); the difference was statistically significant (p<0.05) at cycles 2–7, 10, 13–17, 19, 22, 23 and 26. Indeed, only for the FACT-P SFWB subscale were estimated treatment differences at each cycle not statistically significant.

gr3

Fig. 3 Repeated measures mixed-effects modelling of symptomatic improvement in FACT-P total score.

The results of the joint mixed-effects and log time-to-dropout models suggested that the FACT-P total score profile of abiraterone was superior to that of prednisone (Fig. 4), since the AUC was significantly (p<0.0001) greater with abiraterone and because higher scores indicate better outcomes. The same observation held true for all subscales, except the SFWB subscale (p=0.6932). Corresponding figures for the interim dataset are available in the online appendix.

gr4

Fig. 4 Joint mixed-effects and time-to-dropout modelling of symptomatic improvement in FACT-P total score.

4. Discussion

These results indicate that treatment with abiraterone resulted in a clinically meaningful improvement in patient-reported HRQoL. A larger proportion of patients taking abiraterone than prednisone experienced improvements in all of the FACT-P measures, with the exception of the SFWB subscale. Abiraterone also significantly reduced time to improvement in the PWB subscale and the TOI and significantly delayed deterioration in overall FACT-P and all subscales other than SFWB. Results from two statistical models employed to address missing data supported these observations. In the prednisone control arm of the study, responses were also frequently seen across subscales. This is not surprising, since a number of phase III trials have demonstrated that prednisone and other corticosteroids improve symptoms in patients with advanced prostate cancer [6] and [21].

Though mCRPC patients are known to have rapidly deteriorating HRQoL [2], formal evaluation of HRQoL has only recently become integrated as a routine assessment in large phase III trials in advanced prostate cancer [1], [6], [7], [9], [14], [22], and [23]. With the exception of docetaxel plus prednisone in mCRPC, which showed better FACT-P scores than mitoxantrone plus prednisone [7], none of the evaluated treatments demonstrated improvement in both OS and HRQoL. Data obtained with the androgen receptor antagonist enzalutamide in the same patient population suggest that this agent also improves functional status as measured by FACT-P [24].

The FACT-P questionnaire has been extensively validated and is an accepted instrument for assessing HRQoL in patients with advanced prostate cancer [4], [14], and [16]. The criteria for FACT-P improvement and FACT-P progression provide an objective basis for defining changes that are meaningful to patients; the definition of clinically meaningful change has been consistent across studies [1] and [14]. In this study, the most conservative, upper end of the recommended change thresholds [14] was chosen to ensure that all definitions represented a level of change perceivable by an individual patient and reflected a clinically meaningful change. Since the protocol did not necessitate FACT-P data to be obtained at every treatment cycle, changes that met these thresholds at a single visit were deemed sufficient; requiring consistent changes at two or more consecutive time points would have been impractical. More frequently applied questionnaires may have provided a greater measure of the consistency of individual changes.

The step-wise drop-offs in the KM curves apparent in 3-month intervals reflect the data collection schedule. Since these data were collected once per treatment cycle, they cannot provide information about potential day-to-day changes in HRQoL. More frequent collection of FACT-P questionnaires could be considered for future studies, but may be difficult to achieve in practice.

In this study, patients completed their FACT-P questionnaires during outpatient visits with very high compliance, as shown by the low frequency of missing data points; this clearly demonstrates the feasibility of successfully measuring changes in HRQoL and the importance of including PRO end-points in future clinical trials in this setting. The low rate of missing data also largely negates one potential drawback of PRO studies in advanced cancer populations, namely that those patients who attend the clinic are more likely to be patients who benefit from treatment. The sickest patients with the lowest levels of functioning may be unable to attend study visits and complete the PRO questionnaires, thereby potentially overestimating the overall functioning and well-being of trial participants; this appears not to have been the case in our study. Furthermore, results from the repeated-measures mixed-effects and joint mixed-effects models confirmed that the observed treatment effect was robust and not due to differential dropout rates between the treatment groups.

The magnitude of differences and the strong pattern of effect across multiple FACT-P domains clearly demonstrate that abiraterone plus prednisone was superior to prednisone alone in improving functional status. The only FACT-P measure that consistently failed to exhibit differences between treatment groups was the SFWB subscale, which measures patients’ perception of the support received from their family and social network. The SFWB may influence other FACT-P measures, but is unlikely to be impacted by therapy and may instead depend on non-pharmacological factors, such as family support and social function. Importantly, there was no indication that abiraterone therapy caused deterioration in any functional status domain.

Consistent with the FACT-P results, patient-reported pain and fatigue improvements were also superior in the abiraterone arm [25] and [26]. Since the FACT-P questionnaire includes queries relating to both pain and fatigue, this outcome is not surprising. Indeed, improvements in these two symptoms may account for much of the improvement seen in the FACT-P total score. The difference in median time to functional status deterioration between the treatment arms (5.5months), as measured by the FACT-P total score, was similar to the difference in median survival (4.6months) [11]. It seems likely that much of the HRQoL benefit in abiraterone-treated patients results from a reduction in prostate cancer-related symptoms.

Our analyses have a few important limitations. Most notably, these were exploratory analyses; therefore, despite the fact that data were collected prospectively and that we used a prespecified analysis plan, all p-values must be interpreted carefully. When interpreting the repeated-measures mixed-effects models, it should be noted that missing data are unlikely to be truly missing at random, since patients who die are likely to be those in poorer health at the start of the study and hence have poorer FACT-P scores. Our model relaxes the assumption that data are missing at random. Results were highly consistent between both types of sensitivity analyses.

In conclusion, abiraterone plus prednisone provided substantial HRQoL benefits in patients with mCRPC who have failed docetaxel-based chemotherapy. It improved important functional status parameters more frequently than prednisone alone and significantly prolonged median time to functional deterioration by >5months. These benefits were in addition to a previously reported advantage in OS and were achieved with a good tolerability profile [11] and [12].

Authors’ contributions

Study conception and design: Howard I. Scher, Johann S. de Bono, Arturo Molina, Christopher M. Haqq, Thian Kheoh and Yanni Hao.

Collection and assembly of data: Stephen Harland, John Staffurth, Cora N. Sternberg, Arturo Molina, Thian Kheoh, Howard I. Scher, Karim Fizazi, Christopher J. Logothetis and Johann S. de Bono.

Data analysis and interpretation: Stephen Harland, Arturo Molina, Dennis D. Gagnon, Margaret Rothman, Yanni Hao, Thian Kheoh, Christopher M. Haqq and David Cella.

Figures: Yanni Hao, Dennis D. Gagnon.

Manuscript writing: Stephen Harland, Arturo Molina, Yanni Hao and David Cella (Note: all authors critically reviewed the manuscript for important intellectual content during manuscript development and provided comments accordingly).

Final approval of manuscript: All authors.

Conflict of interest statement

Stephen Harland has received travel support from Janssen R&D and has served as a consultant to Sanofi-Aventis.

John Staffurth has received honoraria from and served as a consultant to Janssen R&D and holds stock in Johnson & Johnson.

Arturo Molina is a full-time employee of Janssen R&D and holds stock in Johnson & Johnson.

Yanni Hao was a full-time employee of Janssen Global Services at the time the study was conducted and holds stock in Johnson & Johnson.

Dennis D. Gagnon was a full-time employee of Truven Health Analytics, which served as consultants to Janssen Global Services in connection with these analyses, at the time the study was conducted.

Cora N. Sternberg has served as a consultant to and received travel support from Ortho Biotech Oncology Research & Development (now Janssen R&D) and has received research funding from Cougar Biotechnology (now Janssen R&D).

David Cella served as a consultant to Janssen R&D in connection with these analyses.

Karim Fizazi has served as a consultant to Amgen, Bayer, Bristol-Myers Squibb, Dendreon, Exelixis, Janssen, Medivation-Astellas, Millennium-Takeda, Novartis and Sanofi Aventis.

Thian Kheoh is a full-time employee of Janssen R&D and holds stock in Johnson & Johnson.

Christopher Haqq was an employee of Cougar Biotechnology (now Janssen R&D) at the time this study was conducted and completed and holds stock in Johnson & Johnson.

Johann S. de Bono has served as a consultant to Janssen R&D, Sanofi Aventis, Medivation, Astellas and Dendreon and is an employee of The Institute of Cancer Research and is supported by Cancer Research UK, both of which have a commercial interest in the development of abiraterone acetate.

Howard Scher has received research funding from the Prostate Cancer Foundation as well as from Aragon, Bristol-Myers Squibb, Exelixis, Janssen R&D, Janssen Global Services and Medivation; has acted as a compensated consultant/advisor to Dendreon, Endo/Orion Pharmaceuticals, Genentech, Novartis, Ortho Biotech Oncology Research & Development (now Janssen R&D; proceeds donated) and has acted as an uncompensated consultant/advisor to Aragon, Celgene, Exelixis, Foundation Medicine, Janssen, Johnson & Johnson Pharmaceutical R&D, Medivation, Millennium Pharmaceuticals and Takeda Pharmaceutical Company. His institution, Memorial Sloan-Kettering Cancer Center, has received research funding from the Prostate Cancer Foundation.

Acknowledgements

The authors would like to thank Margaret Rothman (Janssen Global Services) for her contributions to these analyses and critical review of this manuscript. This trial was funded by Janssen Research & Development (formerly Ortho Biotech Research and Development, a unit of Cougar Biotechnology). Dominik Wolf of PAREXEL provided writing assistance, which was funded by Janssen Global Services. This project was supported by researchers at the National Institute for Health Research University College London Hospitals Biomedical Research Centre.

Role of the funding source: Employees of Janssen participated in trial design, data collection and data analysis and had a supporting role in data interpretation and writing of this report. The sponsor of the study was involved in the design of the trial and provided grants to trial sites and had no other involvement in the conduct of the trial. Editorial support was provided to the authors and funded by the study sponsor, but all decisions relating to manuscript writing and content were made jointly by the authors. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

In addition to the authors, the following investigators (listed in alphabetical order) participated in the COU-AA-301 study: Australia – M. Alam (Liverpool, South Wales), M. Brown (Adelaide), P. Clingan (Wollongong, New South Wales), A. Costello (Parkville, Victoria), I. Davis (Heidelberg, Victoria), P. de Souza (Kogarah, New South Wales), A. Glasgow (Wollongong, New South Wales), L. Horvath (Camperdown, New South Wales), P. Inglis (Herston, Queensland), R. Lynch (Geelong, Victoria), G. Marx (Hornsby, New South Wales), S. Ng (Subiaco), L. Nott (Hobart, Tasmania), M. Nottage (Herston, Queensland), F. Parnis (Kurralta Park), C. Underhill (Wodonga, Victoria), G. Van Hazel (Perth), S. Wong (Footscray, Victoria), Austria – G. Janetschek (Salzberg), W. Loidl (Linz), M. Marberger (Vienna), Belgium – D. Luyten (Hasselt), J. Machiels (Brussels), V. Renard (Gent), S. Rottey (Gent), B. Sautois (Liege), P. Schöffski (Leuven), D. Schrijvers (Antwerp), F. Van Aelst (Roeselare), P. Werbrouck (Kortrijk), W. Wynendaele (Bonheiden), Canada – S. Ernst (London, Ontario), S. Hotte (Hamilton, Ontario), M. Jancewicz (Regina, Saskatchewan), L. Klotz (Toronto, Ontario), J. Michels (Victoria, BC), R. Rajan (Montreal, Quebec), L. Wood (Halifax, Nova Scotia), France – S. Abadie (Angers), F. Joly (Caen), R. Kaplan (Cannes), I. Krakowski (Vandoeuvre Les Nancy), S. Oudard (Paris), F. Rolland (Nantes St. Herblain), S. Zanetta (Dijon), Germany – K. Miller (Berlin), D. Pfister (Aachen), M. Stöckle (Homburg/Saar), H. Suttmann (Hamburg), M. Wirth (Dresden), Hungary – C. Salamon (Pecs), M. Wenczl (Szombathely), Italy – R. Algeri (Grosseto), C. Boni (Reggio Emilia), P. Conte (Modena), L. Cerbone (Rome), E. Villa (Milan), The Netherlands – P. Mulders (Nijmegen), Republic of Ireland – B. Bird (Cork), O. Breathnach (Dublin), F. Janku (Cork), J. McCaffrey (Dublin), R. McDermott (Dublin), S. O’Reilly (Cork), Spain – J. Bellmunt (Barcelona), I. Duran (Madrid), J. Germa Lluch (Barcelona), United Kingdom – G. Durkan (Newcastle-upon-Tyne), T. Elliot (Manchester), P. Hoskin (Northwood, Middlesex), N. James (Birmingham), A. Protheroe (Oxford), J. O’Sullivan (Belfast), J. Waxman (London), United States – L. Appelman (Pittsburgh, PA), E. Arrowsmith (Chattanooga, TN), V. Assikis (Atlanta, GA), A. Baron (San Francisco, CA), W. Berry (Raleigh, NC), J. Burke (Billings, MT), J. Carney (Honolulu, HI), L. Chu (Ft. Myers, FL), N. Cohen (Stamford, CT), T. Cosgriff (Metairie, LA), E. Crane (Cincinnati, OH), B. Curti (Portland, OR), S. Dakhil (Wichita, KS), H. Deshpande (New Haven, CT), S. Denmeade (Baltimore, MD), A. Ferrari (New York, NY), N. Gabrail (Canton, OH), M. Galsky (Las Vegas, NV), D. George (Durham, NC), I. Gore (Birmingham, AL), N. Hahn (Indianapolis, IN), O. Hamid (Los Angeles, CA), J. Harris (West Palm Beach, FL), W. Kelly (New Haven, CT), A. Koletsky (Boca Raton, FL), P. Lara (Sacramento, CA), T. Larson (Robbinsdale, MN), J. McClean (Galesburg, IL), M. Modiano (Tucson, AZ), R. Montgomery (Seattle, WA), L. Nordquist (Omaha, NE), J. Picus (St. Louis, MO), C. Redfern (San Diego, CA), M. Rettig (Los Angeles, CA), S. Riggs (Norfolk, VA), P. Rosen (Beverly Hills, CA), J. Sarantopoulos (San Antonio, TX), A. Sartor (New Orleans, LA), Z. Segota (Ft. Lauderdale, FL), N. Shore (Myrtle Beach, SC), J. Showel (Chicago, IL), M. Smith (Boston, MA), S. Tagawa (New York, NY), S. Tejwani (Detroit, MI), V. Tjan-Wettstein (Bristol, CT), P. Twardowski (Duarte, CA), J. Vacirca (East Setauket, NY), P. VanVeldhuizen (Westwood, KS), M. Vira (New Hyde Park, NY), Y. Wong (Philadelphia, PA), S. Wu (Stony Brook, NY), E. Yu (Seattle, WA).

Appendix A. Supplementary data

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Supplementary data

References

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Footnotes

a UCL Cancer Institute, London, UK UCL Cancer Institute, London, UK

b Cardiff University, Velindre Hospital, Cardiff, UK Cardiff University, Velindre Hospital, Cardiff, UK

c Janssen Research & Development, Los Angeles, CA, USA Janssen Research & Development, Los Angeles, CA, USA

d Janssen Global Services, Raritan, NJ, USA Janssen Global Services, Raritan, NJ, USA

e Truven Health Analytics, Santa Barbara, CA, USA Truven Health Analytics, Santa Barbara, CA, USA

f San Camillo and Forlanini Hospitals, Rome, Italy San Camillo and Forlanini Hospitals, Rome, Italy

g Department of Medical Social Sciences and the Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA Department of Medical Social Sciences and the Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA

h Institut Gustave Roussy, University of Paris Sud, Villejuif, France Institut Gustave Roussy, University of Paris Sud, Villejuif, France

i M.D. Anderson Cancer Center, Houston, TX, USA M.D. Anderson Cancer Center, Houston, TX, USA

j The Institute for Cancer Research and Royal Marsden Hospital, Sutton, UK The Institute for Cancer Research and Royal Marsden Hospital, Sutton, UK

k Memorial Sloan-Kettering Cancer Center and Weill Cornell Medical College, New York, NY, USA Memorial Sloan-Kettering Cancer Center and Weill Cornell Medical College, New York, NY, USA

lowast Corresponding author.

l Co-senior author.

Data presented in part at the European Multidisciplinary Cancer Congress, 2011, September 23–27, 2011, Stockholm, Sweden.

☆☆ Trial registration number: clinicaltrials.gov: NCT00638690.


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