Is Prostate-Specific Antigen a Valid Surrogate End Point for Survival in Hormonally Treated Patients With Metastatic Prostate Cancer? Joint
http://www.100md.com
《临床肿瘤学》
the European Organisation for Research and Treatment of Cancer Data Center, Brussels
Limburgs Universitair Centrum, Diepenbeek, Belgium
AstraZeneca Pharmaceuticals, Macclesfield, United Kingdom
Erasmus Medical Centrum, Rotterdam, the Netherlands
ABSTRACT
PURPOSE: The long duration of phase III clinical trials of overall survival (OS) slows down the treatment-development process. It could be shortened by using surrogate end points. Prostate-specific antigen (PSA) is the most studied biomarker in prostate cancer (PCa). This study attempts to validate PSA end points as surrogates for OS in advanced PCa.
PATIENTS AND METHODS: Individual data from 2,161 advanced PCa patients treated in studies comparing bicalutamide to castration were used in a meta-analytic approach to surrogate end-point validation. PSA response, PSA normalization, time to PSA progression, and longitudinal PSA measurements were considered.
RESULTS: The known association between PSA and OS at the individual patient level was confirmed. The association between the effect of intervention on any PSA end point and on OS was generally low (determination coefficient, < 0.69).
CONCLUSION: It is a common misconception that high correlation between biomarkers and true end point justify the use of the former as surrogates. To statistically validate surrogate end points, a high correlation between the treatment effects on the surrogate and true end point needs to be established across groups of patients treated with two alternative interventions. The levels of association observed in this study indicate that the effect of hormonal treatment on OS cannot be predicted with a high degree of precision from observed treatment effects on PSA end points, and thus statistical validity is unproven. In practice, non-null treatment effects on OS can be predicted only from precisely estimated large effects on time to PSA progression (TTPP; hazard ratio, < 0.50).
INTRODUCTION
Phase III cancer clinical trials that evaluate the clinical benefit of new treatment options often require large patient numbers and long follow-up. Recent advances in the understanding of the biologic mechanisms of disease development have resulted in the emergence of a large number of potentially effective new agents. There is also increasing public pressure for promising new drugs to receive marketing approval as rapidly as possible, in particular for life-threatening diseases such as cancer. For these reasons, there is an urgent need to find ways of shortening the duration of cancer clinical trials. The duration of phase III trials results from the use of long-term clinical end points (clinical progression and survival). Therefore, to replace this end point (the "true" end point) by another ("surrogate") end point that could be measured earlier, more conveniently, or more frequently and would adequately reflect the benefit of new treatments on the clinical end point(s) seems to be an attractive solution.
"Biomarkers" (ie, physical signs or laboratory measurements that occur in association with a pathological process or that have putative diagnostic and/or prognostic utility1) are generally regarded as the best candidate surrogate end points. A biomarker is an intermediate outcome that is correlated with the true clinical outcome for an individual patient. It may be useful for diagnostic or prognostic information on a particular patient. It is a common misconception that established biomarkers necessarily make valid surrogate end points. To this aim, it is required that "the effect of treatment on a surrogate end point must be reasonably likely to predict clinical benefit."2 Thus, "surrogacy" is a concept that relates to groups of patients. To demonstrate surrogacy, a strong association between the treatment effects on the surrogate and on the true end point needs to be established across groups of patients treated with the new and standard interventions.
The validation of a candidate surrogate end point is not straightforward. Until recently, the statistical methods developed for this purpose used the data from a single trial.3-5 These methods suffer from numerous drawbacks: some of them are too stringent to be of practical value, whereas others are based on nontestable assumptions.6,7 To overcome these limitations, a new methodology, known as the "meta-analytic" validation approach, was developed recently.8-10 This method uses large databases from multiple randomized clinical trials and aims at measuring directly the association between the treatment effects on the surrogate and the true end point.
In the field of prostate cancer (PCa), prostate-specific antigen (PSA) has probably been the most studied biomarker. It has been investigated as a potential surrogate end point across disease stages,11-14 and in hormone-refractory patients in particular.15-18 In a recent article, Buyse et al19 considered several PSA-based end points in androgen-independent patients treated with liarozole (an imidazole-like compound that causes elevation of retinoic acid, postulated to have antitumor activity), cyproterone acetate, or flutamide. They showed that despite a strong association at the individual patient level, none of the end points qualified as a surrogate for overall survival (OS). In early PCa, Newling et al20 found a modest correlation between the effect of Casodex on time to PSA progression (TTPP) and on objectively confirmed progression. In primary metastatic PCa, several studies demonstrated some level of association between a post-therapy fall in PSA or a PSA relapse on treatment and long-term survival prognosis.21-25 However, this merely qualifies PSA as a biomarker. In trial NCI-INT-105, treatment differences in post-therapy PSA levels did not translate into survival differences.26 Thus, whether PSA is a valid surrogate for survival in hormonally treated PCa remains an open question. This question is of importance, because the use of PSA could shorten the time to the end point from between several months in advanced disease27 to several years in early disease.28
The objective of the present research is to assess PSA-based end points as surrogates for OS in hormone-na?ve metastatic PCa using the meta-analytic approach. The data from > 2,000 patients treated with bicalutamide (Casodex) that were made available by AstraZeneca Pharmaceuticals were used for this purpose.
PATIENTS AND METHODS
Individual data from three large international randomized trials of AstraZeneca's Casodex Development Program were used (301/302,29,30 306/307,31 and US trial 000132,33; Table 1). In studies 301/302 and 306/307, Casodex monotherapy (50 and 150 mg/day, respectively) was compared to medical or surgical castration. In the US trial, Casodex (50 mg/day) in combination with goserelin or leuprolide acetate was compared to the combination of flutamides (750 mg/day) and castration in a 2 x 2 factorial design. All patients were newly diagnosed with metastatic PCa. Four hundred eighty patients with T3-4 M0 disease and elevated PSA from trial 306/307 were excluded. Survival was an end point in all studies, although time to treatment failure (Table 1) was the primary end point in most. PSA was monitored at months 1, 2 (except US trial), and 3 and then every 3 months until month 18 (trial 301/302) or death (other trials). For the analysis, the PSA test date was assumed to be the visit date.
End Points
We considered OS calculated from randomization to the date of death or last visit as the true end point. PCa-specific survival was defined similarly but with deaths unrelated to PCa or treatment censored at the last visit. PSA response, PSA normalization, TTPP, and the complete series of PSA measurements ("PSA profile") were successively assessed as potential surrogate end points for OS.
Patients who had a baseline PSA level at least five times above the normal range (> 20 ng/mL) were included in the analyses of PSA response and PSA normalization. Patients qualified for PSA response if their PSA declined by at least 50% from baseline level at two subsequent observations at least 4 weeks apart. Patients in whom the decline reached a value below or equal to normal (4 ng/mL) qualified for PSA normalization.25
Two definitions of TTPP were assessed: (1) For TTPP-1, PSA progression was defined as a PSA value above normal (4 ng/mL), representing a first increase 20% above the nadir25 (eg, with a PSA nadir of 2 ng/mL, a minimum increase to 4 ng/mL [100% increase] is required, whereas with a PSA nadir of 3.5 ng/mL, a 20% increase to 4.2 ng/mL is enough). (2) For TTPP-2, PSA progression was defined as a PSA value > 2.5 times the normal range (10 ng/mL), representing a first increase 50% above the moving average (based on three consecutive measurements) nadir. This increase had to be either the last observed value or be sustained for at least 4 weeks19 (eg, with a nadir of 2 ng/mL at three consecutive occasions, a 500% increase to 10 ng/mL is needed to reach the end point, whereas after a nadir of 7 ng/mL, a 50% increase to 10.5 ng/mL is enough).
Patients who died or are alive without PSA progression were censored at the time of death or last visit, respectively.
Statistical Methods
The meta-analytic approach to surrogate end-point validation has been detailed extensively elsewhere.6,9,34-36 Thus, we shall only summarize the key features. The method is rooted in the concept that a valid surrogate end point must enable one to predict with sufficient precision the treatment effect on the true clinical end point (OS) from the observed treatment effect on the surrogate (PSA-based) end point. Unlike traditional validation methods such as the Prentice criteria,3 this new methodology does not require that any of those effects be statistically significant. Indeed, when data from several trials are available, the method consists of simultaneously estimating the relative treatment effects on the survival end point and on the PSA end point (log odds ratio of PSA response or normalization, log hazard ratio [HR] of PSA progression, treatment effect on the longitudinal PSA measurements) in each trial. A model that estimates the association between the treatment effects on the true end point and the corresponding effects on the PSA end points (PSA response,34 TTPP,35 or longitudinal PSA measurements36) in a way similar to standard linear regression (although mathematically more sophisticated) is then adjusted. As in linear regression, the strength of the association is measured by the squared correlation coefficient that we shall denote Rtrial2. This coefficient also indicates the precision with which the treatment effect on the survival end point can be predicted from the observed treatment effect on the surrogate. The maximal possible value of Rtrial2 is 1, which indicates a perfect prediction. In practice, observing Rtrial2 = 1 is not possible, and one rather seeks a value close to 1, which indicates a strong association between the treatment effects and thus a relatively precise prediction.9,35 Additionally, the model quantifies the association between the PSA-based end point and the survival end point at the individual patient level. Parameters quantifying the strength of the association at this level will be denoted by the subscript "patient." They can be regarded as measures of validity of the PSA end point as a biomarker for predicting duration of survival.
Only three trials were available, which is too few to allow a precise estimation of Rtrial2. Therefore, the patients were grouped by the trial they entered and their country of residence, as done by Buyse et al.19 These groups will be henceforth referred to as "trial units."
RESULTS
After excluding nonmetastatic patients and those with no baseline or follow-up PSA measurements, the individual data from 2,161 patients classified into 21 trial units were available for the analysis (Table 2). Their baseline and treatment characteristics are listed in Table 3. More than half of the patients presented with six or more bone metastases. After a median follow-up of 3.25 years, 1,018 patients (52.9%) had died, 815 (71.3%) as a result of PCa (Table 4). The median OS was 2.2 years (95% CI, 2.1 to 2.5) for the Casodex-treated patients and 2.3 years (95% CI, 2.1 to 2.6) in the pooled control groups (Fig 1). The average number of PSA assessments per patient was 6.9 (range, 1 to 23)
PSA Response ( 50% Decline From Baseline) and PSA Normalization
PSA response could be assessed for 1,853 patients. A total of 974 (89.4%) and 687 (90.0%) assessable patients on the Casodex and control groups, respectively, achieved a PSA response (Table 4). Only thirteen trial units representing 1,606 patients were used in the analysis: two trial units were removed because no deaths were observed in the castration group, and six were removed because all patients responded in one or both treatment arms. At the individual level, PSA response was a strong predictor of prolonged survival with a survival odds ratio patient of 1.94 (SE, 0.33), representing a two-fold increase in the odds of surviving beyond any specified time t for the PSA responders compared to the nonresponders. At the trial level, the effects of hormonal intervention on PSA response and on OS were poorly correlated with Rtrial2 = 0.08 (SE, 0.14; 95% CI, 0.0 to 0.49). Figure 2A presents the estimated treatment effects on the response (log odds ratio) and OS (log HR).
One should be careful in interpreting these results, because eight trial units with extreme results were excluded from the analysis.
In 399 (36.6%) and 380 (49.8%) of the assessable patients, the PSA declined to a value 4 ng/mL. Seventeen trial units representing 1,778 patients could be used for this analysis: four were excluded for same reasons as above. At the individual level, the survival odds ratio patient for patients with PSA normalization compared to those without was 4.90 (SE, 0.52), indicating a 4.9-fold greater odds of surviving any specified time t for the patients whose PSA normalized. At the trial level, the treatment effects on PSA and on OS were moderately correlated with Rtrial2 = 0.41 (SE, 0.18; 95% CI, 0.05 to 0.72; Table 5). Figure 2B presents the estimated treatment effects on PSA normalization and OS.
PSA Progression
Nineteen trial units (2,070 patients) and 18 trial units (2,043 patients) could be used for the analysis of TTPP-1 and TTPP-2, respectively (two trial units were excluded from both analyses because of absence of deaths in the castration arm and one from the TTPP-2 analysis because of the absence of PSA progressions in both treatment arms).
The TTPP-1 is presented in Figure 3A: 54.6% of the patients progressed according to this definition (Table 4) within a median time of 11.1 months after being randomly assigned. TTPP-1 was somewhat shorter for the pooled Casodex group than for the control group. TTPP-1 was moderately associated with OS at the individual patient level: the concordance coefficient patient = 0.52 (SE, 0.004) indicates that for each individual patient there is an approximately 50% chance to observe a long (short) OS given a long (short) TTPP. At the trial-unit level, the association between the effects of Casodex on TTPP-1 and on OS was low, with Rtrial2 = 0.21 (SE, 0.17; 95% CI, 0.0 to 0.56; Table 5). This analysis is depicted in Figure 4A, where the treatment effect on survival is regressed against the treatment effect on TTPP-1: the size of the circles represents the trial-unit size. The low trial-level association may be partly because of the outlying data from one trial unit. Excluding this unit from the analysis leaves the individual-level association unchanged (patient = 0.52; SE, 0.004) but increases Rtrial2 to 0.58 (SE, 0.15; 95% CI, 0.20 to 0.81).
Only 31.8% of the patients met the more stringent criterion TTPP-2 (Table 4) at a median time of 24.9 months (Fig 3B). At the patient level, the association of TTPP-2 and OS was somewhat stronger than for TTPP-1, with a concordance coefficient patient = 0.61 (SE, 0.02). The association between the treatment effects on TTPP-2 and OS was somewhat higher than for TTPP-1, with Rtrial2 = 0.66 (SE, 0.13; 95% CI, 0.30 to 0.85; Fig 4B and Table 5).
Longitudinal Measurements of PSA
All previously considered PSA-based end points are summary measures derived from the longitudinal PSA measurements and use only a limited amount of the available information. It thus seemed logical to investigate if the longitudinal series of PSA measurements would not be a better surrogate end point for OS. Figure 5A presents the mean profiles of log-transformed PSA measurements for groups of patients with similar observation time: all profiles eventually end with a PSA increase (progression), and patients with an early progression tend to have a higher initial PSA that does not decrease as much early on.
Figure 5B displays the mean PSA profiles per treatment group: starting from week 52 the curves show a relatively stable linear decrease rather than the increasing curvature observed in Figure 4A. This distortion results from attrition: progressive patients, in whom PSA increases, tend to leave the study, and thus the curve in Figure 5B reflects only those with stable PSA.
In view of Figure 5A, the treatment effect on the log-transformed PSA levels was expressed as a function of time and its square root in a joint model of PSA measurements and survival times. In that model, the individual patient-level association between the PSA process and the hazard of dying is a function of time and cannot be easily summarized into a single measure.35 The results indicated that the correlation between the individual PSA and mortality hazard processes was > 0.90 at any time > 7 months, which suggests a strong association between the PSA profile and the hazard of dying for individual patients. At the trial-unit level, the association between the effect of Casodex on the longitudinal PSA and OS was slightly higher than that for TTPP-2 (Rtrial2 = 0.68; SE, 0.12; Table 5).
DISCUSSION
Using data from the Casodex Development Program, we investigated whether the biomarker PSA could be used to define a valid surrogate for OS in patients with metastatic PCa. The analyses confirm the known value of PSA as a biomarker of prognosis and disease activity (individual-level association). When comparing groups of patients treated with Casodex-based or control treatment, however, the association between the treatment effect on any PSA-based end point and the treatment effect on OS was low in general (Rtrial2 < 0.69 with wide confidence intervals).
The choice of the threshold for Rtrial2 required for a valid surrogate is still a matter of debate.6 Nevertheless, one can argue that the precision of the prediction of the treatment effect on OS from the effect on the PSA-based end points, indicated by the Rtrial2 values observed in the present study, is insufficient to claim any of the assessed PSA-based end points as a statistically valid surrogate end point for OS in phase III clinical trials of hormonal treatment in metastatic PCa.
To illustrate the problem, let us consider a new trial with TTPP as the primary end point (defined as TTPP-2), where data analysis occurs after 400 events and yields an HR of 0.75 for PSA progression (with 400 events, SE [log{HR}] would be of the order of 0.10, resulting in P < .01). Without adjusting for the estimation error in the parameters of the prediction model, one could predict with approximately 95% confidence that the corresponding survival HR would lie within the interval 0.48 to 1.12. Adjustment for the estimation error would widen the confidence interval even further; thus, non-null treatment effects on survival would potentially be identifiable only in large new trials showing a large effect on the PSA end point (eg, HR approximately 0.50 with SE = 0.10).
Buyse et al19 assessed similar PSA-based end points as candidate surrogates for OS in patients with androgen-independent PCa treated with liarozole versus antiandrogen monotherapy. In their study, the association between treatment effects at the trial level were generally low, with Rtrial2 < 0.45 for all tested PSA end points. They concluded that PSA end points could not be regarded as valid surrogates for OS. The reasons for the lack of association in their study may be different than ours; the disease was more advanced, and treatment mode of action differed. In early disease, for which the time savings of using PSA could be greater than in advanced disease, Newling at al20 also found only moderate correlation between the effect of Casodex on PSA progression and objective clinical progression.
Unfortunately, in cancer and other diseases, biomarkers that are strong predictors of the clinical end point for the individual patient often proved to be poor surrogate end points.37-43 Several authors have discussed biologic and medical reasons why biomarkers often fail to validate as surrogate end points.2,37,39,44 The principal explanation is that only a part of the treatment effect on the true clinical end point will be reflected in the biomarker, which may lead to over- or underestimation of the treatment effect on the true end point from the observed effect on the biomarker. Baker and Kramer45 mention that perfect predictors of the true end point at the patient level do not necessarily make good surrogate end points, because the prediction function could differ between randomized treatments and thus would induce incorrect inference on the true end point.
The inability thus far to demonstrate surrogacy for PSA can be explained by several biologic mechanisms. PSA is also produced by normal prostatic tissue, and the amount present may vary between patients. Poorly differentiated tumors may produce proportionally less PSA for the level of tumor burden compared with better differentiated tumors. In addition, PSA is studied in the serum, although the source is prostatic tissue. Conceptually, serum levels can be related to unknown factors promoting or inhibiting leakage from PCa cells into the blood, to cellular levels of PSA and their interindividual variation, and obviously to the total tumor mass present in a given patient.46,47 PSA in itself is an endocrine-dependent enzyme, and its expression is regulated by a promoter that contains androgen-responsive elements.48,49 The treatment effects seen on PSA in trials of endocrine treatment of PCa thus may result, at least in part, from a direct, nontumor-mass–related effect. Such considerations led Scher et al18 to conclude that PSA may not be an appropriate end point for clinical trials of first-line hormonal treatment.
Part of the imprecision in the prediction achieved in our study may be because of the limited number of observations available in each trial unit. The database we used, however, is the largest available. There is also some heterogeneity in trial design between the two monotherapy trials and the combined androgen-blockade trial. However, reanalysis excluding the latter did not change the results.
One also could argue about the use of overall rather than disease-specific survival as the true end point in our study. Analyses using disease-specific survival as the true end point, however, led to essentially similar conclusions (Rtrial2 for TTPP-2 was then 0.49; SE, 0.17; 95% CI, 0.11 to 0.76).
Finally, it was not possible to assess dynamic measures of PSA such as PSA doubling time or PSA velocity in these analyses; as suggested by Kelloff et al50 and D'Amico et al,51 such measures of PSA may carry more information than the ones we could assess.
Our study indicates that PSA surrogacy could not be statistically validated in trials of hormonal treatments against metastatic PCa. However, if large effects on time to PSA end point (HR, < 0.50) could be demonstrated with high precision in a new trial, the results of the present study would still provide evidence of a likely non-null effect (upper bound of the 95% prediction interval for HR, < 1) on OS. This suggests that, in such an instance, TTPP could potentially serve as a basis for accelerated drug approval, together with other trial data documenting safety and other measures of patient benefit, until firm evidence on the basis of the true end point becomes available. Nevertheless, additional research for more powerful surrogate end points in PCa is still needed. Such research should probably focus on dynamic PSA measurements or new, hopefully more specific markers or combinations of markers.
Authors' Disclosures of Potential Conflicts of Interest
Although all authors completed the disclosure declaration, the following authors or their immediate family members indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.
Acknowledgment
We thank AstraZeneca Pharmaceuticals for permission to use the data from the Casodex Development Program from patients with advanced prostate cancer.
NOTES
Authors' disclosures of potential conflicts of interest are found at the end of this article.
REFERENCES
Lesko LJ, Atkinson AJ: Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: Criteria, validation, strategies. Annu Rev Pharmacol Toxicol 41:347-366, 2001
Biomarkers Definitions Working Group: Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharmacol Ther 69:89-95, 2001
Prentice RL: Surrogate endpoints in clinical trials: Definitions and operational criteria. Stat Med 8:431-440, 1989
Freedman LS, Graubard BI, Schatzkin A: Statistical validation of intermediate endpoints for chronic diseases. Stat Med 11:167-178, 1992
Buyse M, Molenberghs G: Criteria for the validation of surrogate endpoints in randomized experiments. Biometrics 54:1014-1029, 1998 [Erratum: Biometrics 56:324, 2000]
Buyse M, Molenberghs G, Burzykowski T, et al: Statistical validation of surrogate endpoints: Problems and proposals. Drug Inf J 34:447-454, 2000
Molenberghs G, Buyse M, Geys H, et al: Statistical challenges in the evaluation of surrogate endpoints in randomized trials. Control Clin Trials 23:607-625, 2002
Daniels MJ, Hugues MD: Meta-analysis for the evaluation of potential surrogate markers. Stat Med 16:1515-1527, 1997
Buyse M, Molenberghs G, Burzykowski T, et al: The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics 1:49-68, 2000
Gail MH, Pfeiffer R, Van Houwelingen HC, et al: On meta-analytic assessment of surrogate outcomes. Biostatistics 1:231-246, 2000
Polascik TJ, Oesterling JE, Partin AW: Prostate specific antigen: A decade of discovery—what we have learned and were we are going. J Urol 162:293-306, 1999
Ornstein DK, Pruthi RS: Prostate-specific antigen. Expert Opin Pharmacother 1:1399-1411, 2000
Schroeder FH. Evidence in advanced disease: The challenge of new therapies. Eur Urol 1:11-16, 2002 (suppl)
Small EJ, Roach M III: Prostate-specific antigen in prostate cancer: A case study in the development of a tumor marker to monitor recurrence and assess response. Semin Oncol 29:264-273, 2002
Sridhara R, Eisenberger MA, Sinibaldi VJ, et al: Evaluation of prostate-specific antigen as a surrogate marker for response of hormone-refractory prostate cancer to suramin therapy. J Clin Oncol 13:2944-2953, 1995
Smith DC, Dunn RL, Stawderman MS, et al: Change in serum prostate-specific antigen as a marker of response to cytotoxic therapy for hormone-refractory prostate cancer. J Clin Oncol 16:1835-1843, 1998
Kelly WK, Scher HI, Mazumdar M, et al: Prostate-specific antigen as a measure of disease outcome in metastatic hormone-refractory prostate cancer. J Clin Oncol 11:607-615, 1993
Scher HI, Kelly WK, Zhang ZF, et al: Post-therapy serum prostate-specific antigen level and survival in patients with androgen-independent prostate cancer. J Natl Cancer Inst 91:244-251, 1999
Buyse M, Vangeneugden T, Bijnens L, et al: Validation of biomarkers and surrogates for clinical endpoints, in JC Bloom (ed): Biomarkers in Clinical Drug Development. New York, NY, Springer-Verlag, 2003, pp 149-168
Newling D, Carroll K, Morris T: Is prostate-specific antigen progression a surrogate for objective clinical progression in early prostate cancer? J Clin Oncol 22:4652, 2004 (suppl 15; abstr 4652)
Matzkin H, Soloway MS, Schellhammer PF, et al: Prognostic factors in stage D2 prostate cancer treated with a pure nonsteroidal antiandrogen. Cancer 72:1286-1290, 1993
Miller JI, Ahmann FR, Drach GW, et al: Serum PSA levels predict duration of remission and survival post hormone therapy of metastatic prostate cancer. J Urol 145:384A, 1991
Newling DW, Denis L, Vermeylen K. Orchiectomy versus goserelin and flutamide in the treatment of newly diagnosed metastatic prostate cancer. Analysis of the criteria of evaluation used in the European Organization for Research and Treatment of Cancer—Genitourinary Group Study 30853. Cancer 72:3793-3798, 1993 (suppl)
Cooper EH, Armitage TG, Robinson MRG, et al: Prostatic specific antigen and the prediction of prognosis in metastatic prostatic cancer. Cancer 66:1025-1028, 1990 (suppl 5)
Collette L, de Reijke TM, Schroeder FH, et al: Prostate specific antigen: a prognostic marker of survival in good prognosis metastatic prostate cancer? (EORTC 30892). Eur Urol 44:182-189, 2003
Eisenberger MA, Blumenstein BA, Crawford ED, et al: Bilateral orchiectomy with or without flutamide for metastatic prostate cancer. N Engl J Med 339:1036-1042, 1998
Newling D, Denis L, Vermeylen K: Orchiectomy versus goserelin and flutamide and the treatment of newly diagnosed metastatic prostate cancer. Cancer 72:3793-3798, 1993
Pound CR, Partin AW, Eisenberger MA, et al: Natural history of progression after PSA elevation following radical prostatectomy. JAMA 281:1591-1597, 1999
Iversen P, Tveter K, Varenhorst E: Randomised study of Casodex 50 mg monotherapy vs orchidectomy in the treatment of metastatic prostate cancer. The Scandinavian Casodex Cooperative Group. Scand J Urol Nephrol 30:93-98, 1996
Kaisary AV, Tyrrell CJ, Beacock C, et al: A randomised comparison of monotherapy with Casodex 50 mg daily and castration in the treatment of metastatic prostate carcinoma. Casodex Study Group. Eur Urol 28:215-222, 1995
Tyrrell CJ, Kaisary AV, Iversen P, et al: A randomised comparison of ‘Casodex’ (bicalutamide) 150 mg monotherapy versus castration in the treatment of metastatic and locally advanced prostate cancer. Eur Urol 33:447-456, 1998
Schellhammer P, Sharifi R, Block N, et al: A controlled trial of bicalutamide versus flutamide, each in combination with luteinizing hormone-releasing hormone analogue therapy, in patients with advanced prostate cancer. Casodex Combination Study Group. Urology 45:745-752, 1995
Schellhammer PF, Sharifi R, Block NL, et al: Clinical benefits of bicalutamide compared with flutamide in combined androgen blockade for patients with advanced prostatic carcinoma: Final report of a double-blind, randomized, multicenter trial. Casodex Combination Study Group. Urology 50:330-336, 1997
Burzykowski T, Molenberghs G, Buyse M: The validation of surrogate endpoints using data from randomized clinical trials: A case study in advanced colorectal cancer. J R Stat Soc [Ser A] 167:103-124, 2004
Burzykowski T, Molenberghs G, Buyse M, et al: Validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. Appl Stat 50:405-422, 2001
Renard D, Geys H, Molenberghs G, et al: Validation of a longitudinally measured surrogate marker for time-to-event endpoint. J Appl Stat 30:235-247, 2003
Fleming TR: Evaluating therapeutic interventions: Some issues and experiences. Stat Sci 7:428-456, 1992
Temple RJ. A regulatory authority's opinion about surrogate endpoints, in Nimmo W, Ticker G (eds), Clinical Measurement in Drug Evaluation. Chichester, United Kingdom, John Wiley, 1995
Fleming TR, DeMets DL: Surrogate end points in clinical trials: Are we being misled? Ann Intern Med 125:605-613, 1996
Buyse M, Thirion P, Carlson RW, et al: Relation between tumour response to first-line chemotherapy and survival in advanced colorectal cancer: A meta-analysis. Meta-Analysis Group in Cancer. Lancet 356:373-378, 2000
The Cardiac Arrhythmia Suppression Trial (CAST) Investigators: Preliminary report: Effect of encainide and flecainide on mortality in a randomized trial of arrhythmia suppression after myocardial infarction. N Engl J Med 321:406-412, 1989
The Cardiac Arrhytmia Suppression Trial (CAST) II Investigators: Effect of the antiarrhythmic agent moricizine on survival after myocardial infarction. N Engl J Med 327:227-233, 1992
Burzykowski T, Molenberghs G, Buyse M, et al: The validation of surrogate endpoints using data from randomized clinical trials: A case study in advanced colorectal cancer. J R Stat Soc [Ser A] 167:103-124, 2004
Frank R, Hargreaves R: Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov 2:566-580, 2003
Baker SG, Kramer BS: A perfect correlate does not a surrogate make. BMC Med Res Methodol 3:16, 2003
Stege RH, Tribukait B, Calrlstron KA, et al: Tissue PSA from fine-needle biopsies of prostatic carcinoma as related to serum PSA, clinical stage, cytological grade and DNA ploidy. Prostate 38:183-188, 1999
Schmid HP, McNeal JE, Stamey TA: Observations on the doubling time of prostate cancer. The use of serial prostate-cancer antigen in patients with untreated disease as a measure of increasing cancer volume. Cancer 71:2031-2040, 1993
Cleutjens KB, van Eekelen CC, van der Kroput HA, et al: Two androgen response regions cooperate in steroid hormone regulated activity of the prostate-specific antigen promoter. J Biol Chem 271:6379-6388, 1996
Riegman PH, Vlietstra RJ, van der Korput JA, et al: The promoter of the prostate-specific antigen gene contains a functional androgen responsive element. Mol Endocrinol 5:1921-1930, 1991
Kelloff GJ, Coffey DS, Chabner BA, et al: Prostate-specific antigen doubling time as a surrogate marker for evaluation of oncologic drugs to treat prostate cancer. Clin Cancer Res 10:3927-3923, 2004
D'Amico AV, Moul JW, Carroll PR, et al: Surrogate end point for prostate cancer-specific mortality after radical prostatectomy or radiation therapy. J Natl Cancer Inst 95:1376-1383, 2003(Laurence Collette, Tomasz)
Limburgs Universitair Centrum, Diepenbeek, Belgium
AstraZeneca Pharmaceuticals, Macclesfield, United Kingdom
Erasmus Medical Centrum, Rotterdam, the Netherlands
ABSTRACT
PURPOSE: The long duration of phase III clinical trials of overall survival (OS) slows down the treatment-development process. It could be shortened by using surrogate end points. Prostate-specific antigen (PSA) is the most studied biomarker in prostate cancer (PCa). This study attempts to validate PSA end points as surrogates for OS in advanced PCa.
PATIENTS AND METHODS: Individual data from 2,161 advanced PCa patients treated in studies comparing bicalutamide to castration were used in a meta-analytic approach to surrogate end-point validation. PSA response, PSA normalization, time to PSA progression, and longitudinal PSA measurements were considered.
RESULTS: The known association between PSA and OS at the individual patient level was confirmed. The association between the effect of intervention on any PSA end point and on OS was generally low (determination coefficient, < 0.69).
CONCLUSION: It is a common misconception that high correlation between biomarkers and true end point justify the use of the former as surrogates. To statistically validate surrogate end points, a high correlation between the treatment effects on the surrogate and true end point needs to be established across groups of patients treated with two alternative interventions. The levels of association observed in this study indicate that the effect of hormonal treatment on OS cannot be predicted with a high degree of precision from observed treatment effects on PSA end points, and thus statistical validity is unproven. In practice, non-null treatment effects on OS can be predicted only from precisely estimated large effects on time to PSA progression (TTPP; hazard ratio, < 0.50).
INTRODUCTION
Phase III cancer clinical trials that evaluate the clinical benefit of new treatment options often require large patient numbers and long follow-up. Recent advances in the understanding of the biologic mechanisms of disease development have resulted in the emergence of a large number of potentially effective new agents. There is also increasing public pressure for promising new drugs to receive marketing approval as rapidly as possible, in particular for life-threatening diseases such as cancer. For these reasons, there is an urgent need to find ways of shortening the duration of cancer clinical trials. The duration of phase III trials results from the use of long-term clinical end points (clinical progression and survival). Therefore, to replace this end point (the "true" end point) by another ("surrogate") end point that could be measured earlier, more conveniently, or more frequently and would adequately reflect the benefit of new treatments on the clinical end point(s) seems to be an attractive solution.
"Biomarkers" (ie, physical signs or laboratory measurements that occur in association with a pathological process or that have putative diagnostic and/or prognostic utility1) are generally regarded as the best candidate surrogate end points. A biomarker is an intermediate outcome that is correlated with the true clinical outcome for an individual patient. It may be useful for diagnostic or prognostic information on a particular patient. It is a common misconception that established biomarkers necessarily make valid surrogate end points. To this aim, it is required that "the effect of treatment on a surrogate end point must be reasonably likely to predict clinical benefit."2 Thus, "surrogacy" is a concept that relates to groups of patients. To demonstrate surrogacy, a strong association between the treatment effects on the surrogate and on the true end point needs to be established across groups of patients treated with the new and standard interventions.
The validation of a candidate surrogate end point is not straightforward. Until recently, the statistical methods developed for this purpose used the data from a single trial.3-5 These methods suffer from numerous drawbacks: some of them are too stringent to be of practical value, whereas others are based on nontestable assumptions.6,7 To overcome these limitations, a new methodology, known as the "meta-analytic" validation approach, was developed recently.8-10 This method uses large databases from multiple randomized clinical trials and aims at measuring directly the association between the treatment effects on the surrogate and the true end point.
In the field of prostate cancer (PCa), prostate-specific antigen (PSA) has probably been the most studied biomarker. It has been investigated as a potential surrogate end point across disease stages,11-14 and in hormone-refractory patients in particular.15-18 In a recent article, Buyse et al19 considered several PSA-based end points in androgen-independent patients treated with liarozole (an imidazole-like compound that causes elevation of retinoic acid, postulated to have antitumor activity), cyproterone acetate, or flutamide. They showed that despite a strong association at the individual patient level, none of the end points qualified as a surrogate for overall survival (OS). In early PCa, Newling et al20 found a modest correlation between the effect of Casodex on time to PSA progression (TTPP) and on objectively confirmed progression. In primary metastatic PCa, several studies demonstrated some level of association between a post-therapy fall in PSA or a PSA relapse on treatment and long-term survival prognosis.21-25 However, this merely qualifies PSA as a biomarker. In trial NCI-INT-105, treatment differences in post-therapy PSA levels did not translate into survival differences.26 Thus, whether PSA is a valid surrogate for survival in hormonally treated PCa remains an open question. This question is of importance, because the use of PSA could shorten the time to the end point from between several months in advanced disease27 to several years in early disease.28
The objective of the present research is to assess PSA-based end points as surrogates for OS in hormone-na?ve metastatic PCa using the meta-analytic approach. The data from > 2,000 patients treated with bicalutamide (Casodex) that were made available by AstraZeneca Pharmaceuticals were used for this purpose.
PATIENTS AND METHODS
Individual data from three large international randomized trials of AstraZeneca's Casodex Development Program were used (301/302,29,30 306/307,31 and US trial 000132,33; Table 1). In studies 301/302 and 306/307, Casodex monotherapy (50 and 150 mg/day, respectively) was compared to medical or surgical castration. In the US trial, Casodex (50 mg/day) in combination with goserelin or leuprolide acetate was compared to the combination of flutamides (750 mg/day) and castration in a 2 x 2 factorial design. All patients were newly diagnosed with metastatic PCa. Four hundred eighty patients with T3-4 M0 disease and elevated PSA from trial 306/307 were excluded. Survival was an end point in all studies, although time to treatment failure (Table 1) was the primary end point in most. PSA was monitored at months 1, 2 (except US trial), and 3 and then every 3 months until month 18 (trial 301/302) or death (other trials). For the analysis, the PSA test date was assumed to be the visit date.
End Points
We considered OS calculated from randomization to the date of death or last visit as the true end point. PCa-specific survival was defined similarly but with deaths unrelated to PCa or treatment censored at the last visit. PSA response, PSA normalization, TTPP, and the complete series of PSA measurements ("PSA profile") were successively assessed as potential surrogate end points for OS.
Patients who had a baseline PSA level at least five times above the normal range (> 20 ng/mL) were included in the analyses of PSA response and PSA normalization. Patients qualified for PSA response if their PSA declined by at least 50% from baseline level at two subsequent observations at least 4 weeks apart. Patients in whom the decline reached a value below or equal to normal (4 ng/mL) qualified for PSA normalization.25
Two definitions of TTPP were assessed: (1) For TTPP-1, PSA progression was defined as a PSA value above normal (4 ng/mL), representing a first increase 20% above the nadir25 (eg, with a PSA nadir of 2 ng/mL, a minimum increase to 4 ng/mL [100% increase] is required, whereas with a PSA nadir of 3.5 ng/mL, a 20% increase to 4.2 ng/mL is enough). (2) For TTPP-2, PSA progression was defined as a PSA value > 2.5 times the normal range (10 ng/mL), representing a first increase 50% above the moving average (based on three consecutive measurements) nadir. This increase had to be either the last observed value or be sustained for at least 4 weeks19 (eg, with a nadir of 2 ng/mL at three consecutive occasions, a 500% increase to 10 ng/mL is needed to reach the end point, whereas after a nadir of 7 ng/mL, a 50% increase to 10.5 ng/mL is enough).
Patients who died or are alive without PSA progression were censored at the time of death or last visit, respectively.
Statistical Methods
The meta-analytic approach to surrogate end-point validation has been detailed extensively elsewhere.6,9,34-36 Thus, we shall only summarize the key features. The method is rooted in the concept that a valid surrogate end point must enable one to predict with sufficient precision the treatment effect on the true clinical end point (OS) from the observed treatment effect on the surrogate (PSA-based) end point. Unlike traditional validation methods such as the Prentice criteria,3 this new methodology does not require that any of those effects be statistically significant. Indeed, when data from several trials are available, the method consists of simultaneously estimating the relative treatment effects on the survival end point and on the PSA end point (log odds ratio of PSA response or normalization, log hazard ratio [HR] of PSA progression, treatment effect on the longitudinal PSA measurements) in each trial. A model that estimates the association between the treatment effects on the true end point and the corresponding effects on the PSA end points (PSA response,34 TTPP,35 or longitudinal PSA measurements36) in a way similar to standard linear regression (although mathematically more sophisticated) is then adjusted. As in linear regression, the strength of the association is measured by the squared correlation coefficient that we shall denote Rtrial2. This coefficient also indicates the precision with which the treatment effect on the survival end point can be predicted from the observed treatment effect on the surrogate. The maximal possible value of Rtrial2 is 1, which indicates a perfect prediction. In practice, observing Rtrial2 = 1 is not possible, and one rather seeks a value close to 1, which indicates a strong association between the treatment effects and thus a relatively precise prediction.9,35 Additionally, the model quantifies the association between the PSA-based end point and the survival end point at the individual patient level. Parameters quantifying the strength of the association at this level will be denoted by the subscript "patient." They can be regarded as measures of validity of the PSA end point as a biomarker for predicting duration of survival.
Only three trials were available, which is too few to allow a precise estimation of Rtrial2. Therefore, the patients were grouped by the trial they entered and their country of residence, as done by Buyse et al.19 These groups will be henceforth referred to as "trial units."
RESULTS
After excluding nonmetastatic patients and those with no baseline or follow-up PSA measurements, the individual data from 2,161 patients classified into 21 trial units were available for the analysis (Table 2). Their baseline and treatment characteristics are listed in Table 3. More than half of the patients presented with six or more bone metastases. After a median follow-up of 3.25 years, 1,018 patients (52.9%) had died, 815 (71.3%) as a result of PCa (Table 4). The median OS was 2.2 years (95% CI, 2.1 to 2.5) for the Casodex-treated patients and 2.3 years (95% CI, 2.1 to 2.6) in the pooled control groups (Fig 1). The average number of PSA assessments per patient was 6.9 (range, 1 to 23)
PSA Response ( 50% Decline From Baseline) and PSA Normalization
PSA response could be assessed for 1,853 patients. A total of 974 (89.4%) and 687 (90.0%) assessable patients on the Casodex and control groups, respectively, achieved a PSA response (Table 4). Only thirteen trial units representing 1,606 patients were used in the analysis: two trial units were removed because no deaths were observed in the castration group, and six were removed because all patients responded in one or both treatment arms. At the individual level, PSA response was a strong predictor of prolonged survival with a survival odds ratio patient of 1.94 (SE, 0.33), representing a two-fold increase in the odds of surviving beyond any specified time t for the PSA responders compared to the nonresponders. At the trial level, the effects of hormonal intervention on PSA response and on OS were poorly correlated with Rtrial2 = 0.08 (SE, 0.14; 95% CI, 0.0 to 0.49). Figure 2A presents the estimated treatment effects on the response (log odds ratio) and OS (log HR).
One should be careful in interpreting these results, because eight trial units with extreme results were excluded from the analysis.
In 399 (36.6%) and 380 (49.8%) of the assessable patients, the PSA declined to a value 4 ng/mL. Seventeen trial units representing 1,778 patients could be used for this analysis: four were excluded for same reasons as above. At the individual level, the survival odds ratio patient for patients with PSA normalization compared to those without was 4.90 (SE, 0.52), indicating a 4.9-fold greater odds of surviving any specified time t for the patients whose PSA normalized. At the trial level, the treatment effects on PSA and on OS were moderately correlated with Rtrial2 = 0.41 (SE, 0.18; 95% CI, 0.05 to 0.72; Table 5). Figure 2B presents the estimated treatment effects on PSA normalization and OS.
PSA Progression
Nineteen trial units (2,070 patients) and 18 trial units (2,043 patients) could be used for the analysis of TTPP-1 and TTPP-2, respectively (two trial units were excluded from both analyses because of absence of deaths in the castration arm and one from the TTPP-2 analysis because of the absence of PSA progressions in both treatment arms).
The TTPP-1 is presented in Figure 3A: 54.6% of the patients progressed according to this definition (Table 4) within a median time of 11.1 months after being randomly assigned. TTPP-1 was somewhat shorter for the pooled Casodex group than for the control group. TTPP-1 was moderately associated with OS at the individual patient level: the concordance coefficient patient = 0.52 (SE, 0.004) indicates that for each individual patient there is an approximately 50% chance to observe a long (short) OS given a long (short) TTPP. At the trial-unit level, the association between the effects of Casodex on TTPP-1 and on OS was low, with Rtrial2 = 0.21 (SE, 0.17; 95% CI, 0.0 to 0.56; Table 5). This analysis is depicted in Figure 4A, where the treatment effect on survival is regressed against the treatment effect on TTPP-1: the size of the circles represents the trial-unit size. The low trial-level association may be partly because of the outlying data from one trial unit. Excluding this unit from the analysis leaves the individual-level association unchanged (patient = 0.52; SE, 0.004) but increases Rtrial2 to 0.58 (SE, 0.15; 95% CI, 0.20 to 0.81).
Only 31.8% of the patients met the more stringent criterion TTPP-2 (Table 4) at a median time of 24.9 months (Fig 3B). At the patient level, the association of TTPP-2 and OS was somewhat stronger than for TTPP-1, with a concordance coefficient patient = 0.61 (SE, 0.02). The association between the treatment effects on TTPP-2 and OS was somewhat higher than for TTPP-1, with Rtrial2 = 0.66 (SE, 0.13; 95% CI, 0.30 to 0.85; Fig 4B and Table 5).
Longitudinal Measurements of PSA
All previously considered PSA-based end points are summary measures derived from the longitudinal PSA measurements and use only a limited amount of the available information. It thus seemed logical to investigate if the longitudinal series of PSA measurements would not be a better surrogate end point for OS. Figure 5A presents the mean profiles of log-transformed PSA measurements for groups of patients with similar observation time: all profiles eventually end with a PSA increase (progression), and patients with an early progression tend to have a higher initial PSA that does not decrease as much early on.
Figure 5B displays the mean PSA profiles per treatment group: starting from week 52 the curves show a relatively stable linear decrease rather than the increasing curvature observed in Figure 4A. This distortion results from attrition: progressive patients, in whom PSA increases, tend to leave the study, and thus the curve in Figure 5B reflects only those with stable PSA.
In view of Figure 5A, the treatment effect on the log-transformed PSA levels was expressed as a function of time and its square root in a joint model of PSA measurements and survival times. In that model, the individual patient-level association between the PSA process and the hazard of dying is a function of time and cannot be easily summarized into a single measure.35 The results indicated that the correlation between the individual PSA and mortality hazard processes was > 0.90 at any time > 7 months, which suggests a strong association between the PSA profile and the hazard of dying for individual patients. At the trial-unit level, the association between the effect of Casodex on the longitudinal PSA and OS was slightly higher than that for TTPP-2 (Rtrial2 = 0.68; SE, 0.12; Table 5).
DISCUSSION
Using data from the Casodex Development Program, we investigated whether the biomarker PSA could be used to define a valid surrogate for OS in patients with metastatic PCa. The analyses confirm the known value of PSA as a biomarker of prognosis and disease activity (individual-level association). When comparing groups of patients treated with Casodex-based or control treatment, however, the association between the treatment effect on any PSA-based end point and the treatment effect on OS was low in general (Rtrial2 < 0.69 with wide confidence intervals).
The choice of the threshold for Rtrial2 required for a valid surrogate is still a matter of debate.6 Nevertheless, one can argue that the precision of the prediction of the treatment effect on OS from the effect on the PSA-based end points, indicated by the Rtrial2 values observed in the present study, is insufficient to claim any of the assessed PSA-based end points as a statistically valid surrogate end point for OS in phase III clinical trials of hormonal treatment in metastatic PCa.
To illustrate the problem, let us consider a new trial with TTPP as the primary end point (defined as TTPP-2), where data analysis occurs after 400 events and yields an HR of 0.75 for PSA progression (with 400 events, SE [log{HR}] would be of the order of 0.10, resulting in P < .01). Without adjusting for the estimation error in the parameters of the prediction model, one could predict with approximately 95% confidence that the corresponding survival HR would lie within the interval 0.48 to 1.12. Adjustment for the estimation error would widen the confidence interval even further; thus, non-null treatment effects on survival would potentially be identifiable only in large new trials showing a large effect on the PSA end point (eg, HR approximately 0.50 with SE = 0.10).
Buyse et al19 assessed similar PSA-based end points as candidate surrogates for OS in patients with androgen-independent PCa treated with liarozole versus antiandrogen monotherapy. In their study, the association between treatment effects at the trial level were generally low, with Rtrial2 < 0.45 for all tested PSA end points. They concluded that PSA end points could not be regarded as valid surrogates for OS. The reasons for the lack of association in their study may be different than ours; the disease was more advanced, and treatment mode of action differed. In early disease, for which the time savings of using PSA could be greater than in advanced disease, Newling at al20 also found only moderate correlation between the effect of Casodex on PSA progression and objective clinical progression.
Unfortunately, in cancer and other diseases, biomarkers that are strong predictors of the clinical end point for the individual patient often proved to be poor surrogate end points.37-43 Several authors have discussed biologic and medical reasons why biomarkers often fail to validate as surrogate end points.2,37,39,44 The principal explanation is that only a part of the treatment effect on the true clinical end point will be reflected in the biomarker, which may lead to over- or underestimation of the treatment effect on the true end point from the observed effect on the biomarker. Baker and Kramer45 mention that perfect predictors of the true end point at the patient level do not necessarily make good surrogate end points, because the prediction function could differ between randomized treatments and thus would induce incorrect inference on the true end point.
The inability thus far to demonstrate surrogacy for PSA can be explained by several biologic mechanisms. PSA is also produced by normal prostatic tissue, and the amount present may vary between patients. Poorly differentiated tumors may produce proportionally less PSA for the level of tumor burden compared with better differentiated tumors. In addition, PSA is studied in the serum, although the source is prostatic tissue. Conceptually, serum levels can be related to unknown factors promoting or inhibiting leakage from PCa cells into the blood, to cellular levels of PSA and their interindividual variation, and obviously to the total tumor mass present in a given patient.46,47 PSA in itself is an endocrine-dependent enzyme, and its expression is regulated by a promoter that contains androgen-responsive elements.48,49 The treatment effects seen on PSA in trials of endocrine treatment of PCa thus may result, at least in part, from a direct, nontumor-mass–related effect. Such considerations led Scher et al18 to conclude that PSA may not be an appropriate end point for clinical trials of first-line hormonal treatment.
Part of the imprecision in the prediction achieved in our study may be because of the limited number of observations available in each trial unit. The database we used, however, is the largest available. There is also some heterogeneity in trial design between the two monotherapy trials and the combined androgen-blockade trial. However, reanalysis excluding the latter did not change the results.
One also could argue about the use of overall rather than disease-specific survival as the true end point in our study. Analyses using disease-specific survival as the true end point, however, led to essentially similar conclusions (Rtrial2 for TTPP-2 was then 0.49; SE, 0.17; 95% CI, 0.11 to 0.76).
Finally, it was not possible to assess dynamic measures of PSA such as PSA doubling time or PSA velocity in these analyses; as suggested by Kelloff et al50 and D'Amico et al,51 such measures of PSA may carry more information than the ones we could assess.
Our study indicates that PSA surrogacy could not be statistically validated in trials of hormonal treatments against metastatic PCa. However, if large effects on time to PSA end point (HR, < 0.50) could be demonstrated with high precision in a new trial, the results of the present study would still provide evidence of a likely non-null effect (upper bound of the 95% prediction interval for HR, < 1) on OS. This suggests that, in such an instance, TTPP could potentially serve as a basis for accelerated drug approval, together with other trial data documenting safety and other measures of patient benefit, until firm evidence on the basis of the true end point becomes available. Nevertheless, additional research for more powerful surrogate end points in PCa is still needed. Such research should probably focus on dynamic PSA measurements or new, hopefully more specific markers or combinations of markers.
Authors' Disclosures of Potential Conflicts of Interest
Although all authors completed the disclosure declaration, the following authors or their immediate family members indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.
Acknowledgment
We thank AstraZeneca Pharmaceuticals for permission to use the data from the Casodex Development Program from patients with advanced prostate cancer.
NOTES
Authors' disclosures of potential conflicts of interest are found at the end of this article.
REFERENCES
Lesko LJ, Atkinson AJ: Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: Criteria, validation, strategies. Annu Rev Pharmacol Toxicol 41:347-366, 2001
Biomarkers Definitions Working Group: Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharmacol Ther 69:89-95, 2001
Prentice RL: Surrogate endpoints in clinical trials: Definitions and operational criteria. Stat Med 8:431-440, 1989
Freedman LS, Graubard BI, Schatzkin A: Statistical validation of intermediate endpoints for chronic diseases. Stat Med 11:167-178, 1992
Buyse M, Molenberghs G: Criteria for the validation of surrogate endpoints in randomized experiments. Biometrics 54:1014-1029, 1998 [Erratum: Biometrics 56:324, 2000]
Buyse M, Molenberghs G, Burzykowski T, et al: Statistical validation of surrogate endpoints: Problems and proposals. Drug Inf J 34:447-454, 2000
Molenberghs G, Buyse M, Geys H, et al: Statistical challenges in the evaluation of surrogate endpoints in randomized trials. Control Clin Trials 23:607-625, 2002
Daniels MJ, Hugues MD: Meta-analysis for the evaluation of potential surrogate markers. Stat Med 16:1515-1527, 1997
Buyse M, Molenberghs G, Burzykowski T, et al: The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics 1:49-68, 2000
Gail MH, Pfeiffer R, Van Houwelingen HC, et al: On meta-analytic assessment of surrogate outcomes. Biostatistics 1:231-246, 2000
Polascik TJ, Oesterling JE, Partin AW: Prostate specific antigen: A decade of discovery—what we have learned and were we are going. J Urol 162:293-306, 1999
Ornstein DK, Pruthi RS: Prostate-specific antigen. Expert Opin Pharmacother 1:1399-1411, 2000
Schroeder FH. Evidence in advanced disease: The challenge of new therapies. Eur Urol 1:11-16, 2002 (suppl)
Small EJ, Roach M III: Prostate-specific antigen in prostate cancer: A case study in the development of a tumor marker to monitor recurrence and assess response. Semin Oncol 29:264-273, 2002
Sridhara R, Eisenberger MA, Sinibaldi VJ, et al: Evaluation of prostate-specific antigen as a surrogate marker for response of hormone-refractory prostate cancer to suramin therapy. J Clin Oncol 13:2944-2953, 1995
Smith DC, Dunn RL, Stawderman MS, et al: Change in serum prostate-specific antigen as a marker of response to cytotoxic therapy for hormone-refractory prostate cancer. J Clin Oncol 16:1835-1843, 1998
Kelly WK, Scher HI, Mazumdar M, et al: Prostate-specific antigen as a measure of disease outcome in metastatic hormone-refractory prostate cancer. J Clin Oncol 11:607-615, 1993
Scher HI, Kelly WK, Zhang ZF, et al: Post-therapy serum prostate-specific antigen level and survival in patients with androgen-independent prostate cancer. J Natl Cancer Inst 91:244-251, 1999
Buyse M, Vangeneugden T, Bijnens L, et al: Validation of biomarkers and surrogates for clinical endpoints, in JC Bloom (ed): Biomarkers in Clinical Drug Development. New York, NY, Springer-Verlag, 2003, pp 149-168
Newling D, Carroll K, Morris T: Is prostate-specific antigen progression a surrogate for objective clinical progression in early prostate cancer? J Clin Oncol 22:4652, 2004 (suppl 15; abstr 4652)
Matzkin H, Soloway MS, Schellhammer PF, et al: Prognostic factors in stage D2 prostate cancer treated with a pure nonsteroidal antiandrogen. Cancer 72:1286-1290, 1993
Miller JI, Ahmann FR, Drach GW, et al: Serum PSA levels predict duration of remission and survival post hormone therapy of metastatic prostate cancer. J Urol 145:384A, 1991
Newling DW, Denis L, Vermeylen K. Orchiectomy versus goserelin and flutamide in the treatment of newly diagnosed metastatic prostate cancer. Analysis of the criteria of evaluation used in the European Organization for Research and Treatment of Cancer—Genitourinary Group Study 30853. Cancer 72:3793-3798, 1993 (suppl)
Cooper EH, Armitage TG, Robinson MRG, et al: Prostatic specific antigen and the prediction of prognosis in metastatic prostatic cancer. Cancer 66:1025-1028, 1990 (suppl 5)
Collette L, de Reijke TM, Schroeder FH, et al: Prostate specific antigen: a prognostic marker of survival in good prognosis metastatic prostate cancer? (EORTC 30892). Eur Urol 44:182-189, 2003
Eisenberger MA, Blumenstein BA, Crawford ED, et al: Bilateral orchiectomy with or without flutamide for metastatic prostate cancer. N Engl J Med 339:1036-1042, 1998
Newling D, Denis L, Vermeylen K: Orchiectomy versus goserelin and flutamide and the treatment of newly diagnosed metastatic prostate cancer. Cancer 72:3793-3798, 1993
Pound CR, Partin AW, Eisenberger MA, et al: Natural history of progression after PSA elevation following radical prostatectomy. JAMA 281:1591-1597, 1999
Iversen P, Tveter K, Varenhorst E: Randomised study of Casodex 50 mg monotherapy vs orchidectomy in the treatment of metastatic prostate cancer. The Scandinavian Casodex Cooperative Group. Scand J Urol Nephrol 30:93-98, 1996
Kaisary AV, Tyrrell CJ, Beacock C, et al: A randomised comparison of monotherapy with Casodex 50 mg daily and castration in the treatment of metastatic prostate carcinoma. Casodex Study Group. Eur Urol 28:215-222, 1995
Tyrrell CJ, Kaisary AV, Iversen P, et al: A randomised comparison of ‘Casodex’ (bicalutamide) 150 mg monotherapy versus castration in the treatment of metastatic and locally advanced prostate cancer. Eur Urol 33:447-456, 1998
Schellhammer P, Sharifi R, Block N, et al: A controlled trial of bicalutamide versus flutamide, each in combination with luteinizing hormone-releasing hormone analogue therapy, in patients with advanced prostate cancer. Casodex Combination Study Group. Urology 45:745-752, 1995
Schellhammer PF, Sharifi R, Block NL, et al: Clinical benefits of bicalutamide compared with flutamide in combined androgen blockade for patients with advanced prostatic carcinoma: Final report of a double-blind, randomized, multicenter trial. Casodex Combination Study Group. Urology 50:330-336, 1997
Burzykowski T, Molenberghs G, Buyse M: The validation of surrogate endpoints using data from randomized clinical trials: A case study in advanced colorectal cancer. J R Stat Soc [Ser A] 167:103-124, 2004
Burzykowski T, Molenberghs G, Buyse M, et al: Validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. Appl Stat 50:405-422, 2001
Renard D, Geys H, Molenberghs G, et al: Validation of a longitudinally measured surrogate marker for time-to-event endpoint. J Appl Stat 30:235-247, 2003
Fleming TR: Evaluating therapeutic interventions: Some issues and experiences. Stat Sci 7:428-456, 1992
Temple RJ. A regulatory authority's opinion about surrogate endpoints, in Nimmo W, Ticker G (eds), Clinical Measurement in Drug Evaluation. Chichester, United Kingdom, John Wiley, 1995
Fleming TR, DeMets DL: Surrogate end points in clinical trials: Are we being misled? Ann Intern Med 125:605-613, 1996
Buyse M, Thirion P, Carlson RW, et al: Relation between tumour response to first-line chemotherapy and survival in advanced colorectal cancer: A meta-analysis. Meta-Analysis Group in Cancer. Lancet 356:373-378, 2000
The Cardiac Arrhythmia Suppression Trial (CAST) Investigators: Preliminary report: Effect of encainide and flecainide on mortality in a randomized trial of arrhythmia suppression after myocardial infarction. N Engl J Med 321:406-412, 1989
The Cardiac Arrhytmia Suppression Trial (CAST) II Investigators: Effect of the antiarrhythmic agent moricizine on survival after myocardial infarction. N Engl J Med 327:227-233, 1992
Burzykowski T, Molenberghs G, Buyse M, et al: The validation of surrogate endpoints using data from randomized clinical trials: A case study in advanced colorectal cancer. J R Stat Soc [Ser A] 167:103-124, 2004
Frank R, Hargreaves R: Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov 2:566-580, 2003
Baker SG, Kramer BS: A perfect correlate does not a surrogate make. BMC Med Res Methodol 3:16, 2003
Stege RH, Tribukait B, Calrlstron KA, et al: Tissue PSA from fine-needle biopsies of prostatic carcinoma as related to serum PSA, clinical stage, cytological grade and DNA ploidy. Prostate 38:183-188, 1999
Schmid HP, McNeal JE, Stamey TA: Observations on the doubling time of prostate cancer. The use of serial prostate-cancer antigen in patients with untreated disease as a measure of increasing cancer volume. Cancer 71:2031-2040, 1993
Cleutjens KB, van Eekelen CC, van der Kroput HA, et al: Two androgen response regions cooperate in steroid hormone regulated activity of the prostate-specific antigen promoter. J Biol Chem 271:6379-6388, 1996
Riegman PH, Vlietstra RJ, van der Korput JA, et al: The promoter of the prostate-specific antigen gene contains a functional androgen responsive element. Mol Endocrinol 5:1921-1930, 1991
Kelloff GJ, Coffey DS, Chabner BA, et al: Prostate-specific antigen doubling time as a surrogate marker for evaluation of oncologic drugs to treat prostate cancer. Clin Cancer Res 10:3927-3923, 2004
D'Amico AV, Moul JW, Carroll PR, et al: Surrogate end point for prostate cancer-specific mortality after radical prostatectomy or radiation therapy. J Natl Cancer Inst 95:1376-1383, 2003(Laurence Collette, Tomasz)