Affiliations of authors: H. I. Scher, W. M. K. Kelly, Genitourinary Oncology Service, Division of Solid Tumor Oncology, Department of Medicine, Memorial Sloan-Kettering Cancer Center, and Department of Medicine, Cornell University Medical College, New York, NY; Z.-F. Zhang, M. Sun (Division of Biostatistics, Department of Epidemiology and Biostatistics), M. Schwartz (Department of Laboratory Medicine), Memorial Sloan-Kettering Cancer Center; P. Ouyang, C. Ding, W. Wang, I. D. Horak, A. B. Kremer, Janssen Pharmaceutica, Inc., Titusville, NJ.
Correspondence to: Howard I. Scher, M.D., Memorial Sloan-Kettering Cancer Center, 1275 York Ave., New York, NY 10021.
Present address: Z.-F. Zhang, Department of Epidemiology, School of Public Health and Johnson Comprehensive Cancer Center, University of California at Los Angeles, CA.
Present address:M. Sun, Department of Epidemiology, School of Public Health and Johnson Comprehensive Cancer Center, University of California at Los Angeles, CA.
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ABSTRACT |
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INTRODUCTION |
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Our original report (4) showing an association between a post-therapy decline in PSA levels and survival was unique in that it included multivariate techniques that were validated by the use of an independent dataset. Since then, numerous reports (5-14) have appeared with seemingly conflicting results. Contributing to the conflicts are inconsistencies in the "criteria" used to classify a patient into a category of "response" (presumed to be of benefit to the patient) or "no response" (no benefit). This situation was shown when we categorized the same cohort of patients by use of different criteria of post-therapy PSA changes; the proportion of "responders" ranged from 5% to 45% (15). The statistical techniques applied also differed, but most noteworthy is that few analyses used multivariate techniques and an independent dataset for validation. It is also likely that a PSA-based outcome measure may be appropriate for some, but not for other, classes of drug and that different post-therapy PSA change definitions would be needed for drugs that act by different mechanisms.
The more important consideration is that it was not our intent that a
single "categorical" definition of post-therapy PSA change (i.e.,
no change, 50% decline, or
80% decline from baseline)
be a surrogate for "response." Rather, we intended to provide a reproducible
trial end point that could be used to decide whether an approach was of
sufficient benefit to justify continued clinical development and, if so, for
sample size considerations in the design of prospective confirmatory phase II
and phase III trials.
In this study, we explored associations between pretherapy variables including the rate of rise in PSA levels prior to treatment, different definitions of a post-therapy PSA decline, and survival in a larger cohort of patients treated at the Memorial Sloan-Kettering Cancer Center (MSKCC) with the aim of standardizing reporting of outcomes. A multivariate model to predict survival was then derived. We tested the model and validated it on an independent dataset of patients enrolled in two randomized phase III trials that compared liarozole, a drug that inhibits retinoic acid metabolism (16), with prednisone (the USA 22 trial) and liarozole with cyproterone acetate, a type I mixed progestational anti-androgen (the INT 5 trial).
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PATIENTS AND METHODS |
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Two hundred fifty-four patients with androgen-independent prostate cancer treated on 11 consecutive studies from 1987 through 1994 at MSKCC were included. All had documented progressive disease despite castrate levels of testosterone defined as 1) a rising PSA level of 50% or greater from baseline on three successive occasions, 2) new metastatic lesions on bone scan, or 3) a 25% or greater increase in a bidimensionally measurable tumor mass. Although the individual treatment programs differed with respect to the requirement for measurable or evaluable disease, the pretreatment evaluations were similar. A complete medical history was obtained from all patients. All patients also underwent a physical examination. Laboratory studies included an automated blood cell and platelet count, 12 channel screening profiles (alkaline phosphatase, lactate dehydrogenase, aspartate transglutaminase, blood urea nitrogen, creatinine, calcium, phosphorus, glucose, uric acid, total protein, albumin, and total bilirubin), measurement of serum acid phosphatase, and PSA measurement. Imaging studies included an abdominal and pelvic computed tomography or magnetic resonance imaging and bone scan. Patients were treated according to the individual program on which they were enrolled.
In general, therapeutic outcomes were assessed at 8-week intervals unless clinically indicated. Post-therapy PSA determinations were performed at a minimum of every 4 weeks. All patients were followed until death or censored at last follow-up. Case patients who were enrolled in more than one study were evaluated only on their first treatment.
All clinical trial data were obtained in full compliance with MSKCC institutional review board requirements for studies involving human subjects. All patients enrolled in MSKCC-approved research investigations provide written informed consent before being enrolled in an MSKCC clinical trial.
Analysis of Prognostic Factors
Variables explored included performance status (Karnofsky) at
baseline, sites of disease, baseline measurements, pretherapy PSA
incline slope, and post-therapy PSA decline (50% from
baseline). Demographics included age as a dichotomous (>70 years old
versus
70 years old) or continuous variable and histologic grade
of the tumor at the time of initial diagnosis. Baseline measurements
included serum PSA levels (ng/mL), serum acid phosphatase levels (U/L),
serum alkaline phosphatase levels (U/L), serum lactate dehydrogenase
levels (U/L), serum hemoglobin levels (g/dL), serum creatinine levels
(mg/dL), serum albumin levels (g/dL), serum glutamic-oxalacetic
transaminase levels (U/L), and serum testosterone levels (ng/dL). On
the basis of our previous analysis showing no association, prior
radiation therapy, prior surgery, type of primary hormone therapy, and
duration of response to primary therapy were not considered.
The variable pretherapy PSA rate of rise was designed to estimate rates of progression prior to protocol treatment. It was calculated for an individual patient by fitting a regression line to a minimum of three or more PSA determinations (if available) obtained prior to treatment, if there was no change in treatment during the interval over which the PSA measurements were obtained. The slope of the fitted line was used as the independent variable, and survival was used as the dependent variable in a regression model.
After treatment, PSA values were plotted, and patients were categorized
into one of two mutually exclusive categories based on whether or not
they achieved a post-therapy PSA decline (50%) by use of three
different methods of assignment. The first method of assignment
required two consecutive PSA declines, each less than 50% of the
baseline value, achieved by, but not necessarily maintained past, the
landmark period of 8 or 12 weeks. The second method required two
consecutive PSA declines, each less than 50% of the baseline value,
which was maintained at or past the landmark period of 8 or 12 weeks.
The third method required two consecutive PSA declines, each less than
50% of the baseline value, which was maintained for a minimum of 8
weeks. This condition had to be satisfied by week 12 (the landmark
period). These landmarks were considered because the intervals
correspond to the points at which "response" assessments are
typically performed in phase II trials.
Statistical Analysis
The survival distributions of the pretherapy parameters analyzed as
categorical variables were compared by the logrank test (17)
with a level of statistical significance .05. Most cut points were
based on the upper limits of normal for the parameter. To avoid
subjectivity in selecting a break point, we also evaluated continuous
variables by the Wald test using the proportional hazards model.
Furthermore, to reduce the potential for bias in the evaluation of
patients who met the criteria for PSA decline versus those who did not,
8- and 12-week landmarks were investigated (vide supra)
(18). Patients whose survival was shorter than the landmark
point were excluded from the analysis. "PSA response" was
evaluated according to the above-mentioned methods of assignment by use
of a computer program. Patients were then followed forward in time to
ascertain whether survival from the landmark depended on whether or not
the defined post-therapy change in PSA levels for that patient met the
criteria established for the 8- and 12-week time periods.
Survival distributions were estimated by the Kaplan-Meier method (19), and median survival times were reported from the start of therapy. For these calculations, the LIFETEST procedure in Statistical Analysis System was used (20). All patients who died were considered treatment failures, whereas patients alive at last follow-up were classified as censored in the survival analysis. Proportional hazards analysis was used to obtain maximum likelihood estimates of relative risks and their 95% confidence intervals (CIs) in univariate and multivariate analyses (21,22). All parameters significant at the .05 level in univariate analysis were evaluated in the multiple proportional hazard model. Stepwise, backward and forward iterative processes were employed to obtain the final prognostic model. All reported P values are two-sided.
Validation of MSKCC Model With the Use of an Independent Dataset
To validate the final model derived from the MSKCC population, we used a combined independent dataset of patients enrolled in two randomized phase III trials that compared liarozole (16) and prednisone (USA 22 trial) and liarozole and cyproterone acetate (INT 5 trial). In these trials, PSA measurements were obtained at 4-week intervals.
The validation process involved the calculation of a risk score (R) for each patient in the validation dataset using the final model obtained from the MSKCC data by use of the following equation:
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where the coefficients (ß) are estimated from the proportional hazards model obtained from the MSKCC data and Xnrepresents the significant prognostic factor(s) in the model. Patients were then divided on the basis of the R score into three equal groups. Within each of the three risk groups, Kaplan-Meier estimates of survival were computed. These estimates were then compared with the MSKCC model-based estimates of survival.
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RESULTS |
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There were a total of 254 patients followed for a median of 39
months (range, 1-58.2 months); of these patients, 200 (79%) died
of prostate cancer (Tables 1 and
2).
The median age of the
patients was 67 years. Prior therapy included one hormonal manipulation
in 49.6% and two in 35.8%, while 12.2% had three or more
hormonal interventions. Protocol therapy included suramin in 39.4%,
rhenium-186 hydroxyethylidene diphosphonate in 28.0%, Casodex (high
dose) in 16.1%, 13-cis-retinoic acid and interferon alfa
in 6.3%, edatrexate in 5.5%, and all-trans-retinoic
acid in 4.7% of cases. The baseline biochemical parameters
reflected a range of disease severity that was also shown by the median
baseline PSA level of 96 ng/mL (range, 0-8450 ng/mL). The median
survival of the population was 12.9 months (95% CI = 11.3-15.8
months), of whom 89% survived 12 weeks or more, and 13% showed
a 50% or greater decline in PSA levels from baseline following the
criteria defined for the 12-week landmark using the first method of
assignment (vide supra).
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The results of the univariate survival analysis are presented in
Table 3. Older age was associated with a longer
survival whether analyzed as a dichotomous variable (>70 years of
age versus
70 years of age; median survival of 15.4 months versus
11.3 months, respectively) but was statistically significant only when
analyzed as a continuous variable in the proportional hazards model
(P = .045).
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Pretherapy PSA rate of rise. A minimum of three (median, four) consecutive pretherapy PSA measurements was available for 158 patients to calculate a pretherapy slope by use of a fitted regression line. The calculated slope was then used in a regression model to explore associations with survival. No association was observed (P = .46).
Baseline PSA. When evaluated as a continuous variable in the proportional hazards model, baseline PSA levels were predictive of survival (P = .0011). As a discrete variable, patients with a baseline PSA level of greater than 100 ng/mL had inferior outcomes relative to those with a baseline PSA level of 100 ng/mL or below; the median survivals were 10.6 months versus 17.1 months, respectively (P = .0006).
Post-therapy PSA decline. The median survival for 26 patients who met the criteria for a 50% or greater decline in PSA levels from baseline at 8 weeks (11% of the population) was 23.6 months versus 12.3 months for those who did not show a post-therapy PSA decline (logrank test: P = .0002). With the use of a 12-week landmark, the respective median survivals were 25.3 months versus 13 months (logrank test: P = .0001). Similar results were obtained with the use of the second and third methods of assignment for a post-therapy decline in PSA levels.
Multivariate Analysis
All factors that were statistically significant at the .05 level in
the univariate analysis were included in the multivariate proportional
hazards model using stepwise, forward and backward processes to obtain
the final model. The results, presented in Table 4,
were similar with each modeling technique. As shown, a 50% or
greater decline in PSA levels within 12 weeks was a statistically
significant predictor of survival (risk ratio = 2.31; 95% CI =
1.39-3.81). An increased baseline serum lactate dehydrogenase level
was a negative predictor, whereas a higher baseline serum hemoglobin
level and older age were associated with improved outcomes and were
more predictive than post-therapy PSA decline. Once these factors were
included, no other factors retained statistical significance.
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Univariate analysis of PSA decline. We employed a combined
independent dataset of 541 patients from two randomized phase III
trials to validate the final model from MSKCC data. Two patients were
excluded because of missing survival data. The baseline demographics
for the populations enrolled on each of two randomized comparisons as
well as for the entire population are included in Table
5 and, with the exception of age, were similar to
those for the MSKCC cohort. Four hundred three patients (75%) died
of disease during the study period with a median survival of 11.4
months compared with the 12.9-month median survival for the MSKCC
population (see Table 2
). The median age for this cohort of
patients was 72 years (range, 46-94 years), which was older than that
for the MSKCC cohort (median age, 67 years; range, 45-86 years)
(see Table 1
).
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Validation of PSA decline with the use of the independent
dataset. The four major predictors (i.e., 50% decline in
PSA values at 12 weeks, baseline lactate dehydrogenase level, baseline
hemoglobin level, and age) identified in the multivariate model
developed from the MSKCC population were used to calculate the risk of
death for each patient in the independent dataset. The final model was
as follows:
1) Rj = 0.834864 x 50% PSA decline within 12 weeks + 0.002504 x baseline lactate dehydrogenase - 0.193959 x baseline hemoglobin - 0.022341 x age.
By use of this model, an 80-year-old man who had a pretreatment lactate dehydrogenase level of 230 U/L and a hemoglobin level of 13 g/dL and who did not have a post-therapy PSA decline (coded as 1) would have the following risk score:
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2) The survival function Sj for each patient
in the validation dataset was then estimated, and the entire population
was divided into three equal groups according to risk score. Patients
with a risk score less than -2.856 were considered at low risk;
those with risk scores between -2.856 and -2.391 were considered
at intermediate risk, and those with risk scores above -2.391 were
considered at high risk. The median survival times were 23, 17, and 9
months for the low-, intermediate-, and high-risk groups, respectively.
The average of the survival function for each event point for each risk
group was then calculated. Fig. 1 shows the
predicted survival function for three risk groups classified by use of
the MSKCC-derived model plotted against the observed Kaplan-Meier
curve for each risk group. The predicted and observed 1-, 2-, and
3-year survival rates are shown in Table 6.
Similar
survival rates were identified at 1 year and at 2 years. In year 3,
some discrepancy was observed and may reflect the small number of
patients alive at that point in time.
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DISCUSSION |
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The association between a 50% or greater decline in PSA levels and survival was observed by use of the MSKCC dataset irrespective of whether an 8- or 12-week landmark was considered and with two or three values meeting the defined criteria of decline. The post-therapy decline definition corresponded to two measurements at 4-week intervals for the independent dataset. The pretherapy rise in PSA levels, which was used as an index of disease aggressiveness, did not have an impact on outcomes in this dataset. Baseline performance status did not add to the association when hemoglobin level, lactate dehydrogenase level, and age were considered. The lack of an association in this model does not exclude the importance of this parameter in relation to other factors in other models.
A post-therapy decline in PSA levels of 50% or greater was not the only factor having an impact on outcome. Other factors of statistical significance were the baseline serum lactate dehydrogenase level, baseline hemoglobin level, and age. When these factors were considered, performance status at baseline did not add predictive value, which may be a function of the small number of patients with a poor performance status at baseline enrolled in these trials (14). Similarly, although an improved outcome was observed for patients with disease limited to soft tissue only versus soft tissue and bone or bone alone, these parameters did not enter the final model.
Not all groups have observed the association between a post-therapy decline in PSA levels and survival (11,25,26). Two of the trials included patients treated with suramin (11,26), an agent known to inhibit PSA release from cells (27), resulting in an appropriate note of caution regarding the use of this end point in clinical trials (28). A similar lack of association between a decline in PSA levels and survival was observed in a large randomized trial of patients with hormone-naive disease addressing the question of whether the addition of flutamide to orchiectomy results in an improved outcome relative to orchiectomy alone (29). It is known, however, that androgens function through an androgen response element on the PSA promoter to increase expression of the gene. Ablating androgens decreases expression (30-33). Does the finding of an effect of a drug or a particular type of treatment mean that a post-therapy decline in PSA levels cannot be used as an end point for phase II screening trials in all stages of the disease? No, but it suggests that it may not be appropriate for clinical trials of hormonal treatments in patients who are hormone naive or who have noncastrate levels of testosterone. What about suramin and hydrocortisone in androgen-independent disease? In one trial showing no association, the categorization of patients as responders or nonresponders was based on a single PSA measurement at 4 weeks relative to baseline (11). This is the time frame in which transient declines in PSA levels that are not durable can be observed (34). In the present study, the association was observed, although 40% of the population received suramin. A difference was that patients in our study were not categorized until week 8 or week 12, a more typical time for outcome assessments, which may have reduced the chance of classifying patients who did not show sustained declines into a "response" or "benefit" category. An additional difference was that we required that the decline be documented on more than one occasion, which, on the basis of the validation set in the current trial, translated into two determinations beginning at week 4 or week 8 and repeated 4 weeks later.
The issue of the androgen regulation of PSA is less applicable to
tumors that are proliferating despite castrate levels of testosterone.
It has long been recognized that, based on similar estimates of tumor
burden, levels of PSA are quantitatively lower in patients with
androgen-independent as opposed to androgen-dependent disease
(35). Castration results in a lower "set point" at
baseline, similar to that described for patients with benign prostatic
hyperplasia enrolled in trials in which the 5-reductase inhibitor
finasteride was used (36). Androgens are not the only agents
that can affect PSA gene expression, synthesis, or release into the
circulation without affecting cell proliferation or cell kill. Other
agents include phorbol esters (37), differentiating agents
such as the retinoids (38,39), phenylbutyrate and
phenylacetate (40,41), the Chinese herbal preparation PC-SPES
(42), and the calcium channel influx inhibitor
carboxyamidatriazole (43). Most can be anticipated by first
evaluating the effect of the compound on PSA in vitro and then
inspecting the patterns of change after treatment in phase I
investigations (34). In general, following treatment with a
cytotoxic drug, durable declines in PSA levels are associated with
other measures of benefit, including regressions in measurable disease,
improvements in radionuclide bone scans, and a decrease in
cancer-related pain. In contrast, successful differentiation of a cell
may result in an increase in PSA levels followed by a decline when
overall cell mass is reduced (34). In trials of this class of
compounds, a different post-therapy PSA decision rule will be required
for screening phase II investigations.
The present study explored only a 50% or greater decline from baseline as the outcome measure, in part because of the level of efficacy of the agents explored. Other decision rules, i.e., a 75% or greater or an 80% or greater decline, have been proposed as more "valid." Lost in these discussions is the fact that defining an "optimal" post-therapy pattern of change does not address the issue of how much of the observed association between a given degree of change in PSA levels and survival can be explained on the basis of the post-therapy change in PSA levels alone. Until this issue has been addressed, the critical question is whether satisfying a proposed decision rule, be it a given degree of decline, no change, or slowing of the rate of rise, in a predetermined proportion of patients in a phase II trial justifies the continued development of a compound or combination of agents. It is our view that it does, with the recognition that treatment is rarely stopped when the PSA level is declining. Nevertheless, because there are other post-therapy changes in PSA that may prove to have prognostic significance, we suggest that the expression "PSA response" not be used and that investigators define the decision rule utilized in their respective trials. Avoiding the expression "PSA response" will allow all groups to place their results in the context of others as we await prospective validation studies.
The observation that patients with soft tissue and osseous disease had outcomes similar to those of patients with osseous disease alone suggests that phase II clinical trials need not be restricted to patients with measurable disease. At this stage, however, it remains essential that associations between changes in PSA levels with other measures of outcome, such as changes in the physical examination, radiographs, scans, and symptoms, continue to be assessed. It is further recommended that PSA measurements be performed in the same laboratory and that changes be reported on the basis of the percent decline, the number of times this decline is documented, the interval between measurements, and the duration of time the decline is maintained so that results can be analyzed and interpreted in a consistent manner (34). Ultimately, any "criteria" proposed will need to be prospectively validated in phase III trials (23).
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NOTES |
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Supported by Public Health Service grants CA05826 and CA09207 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services; by CaPCURE; by The Tarnapol Foundation; and by the PepsiCo Foundation.
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REFERENCES |
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---|
1 Lilja H. A kallikrein-like serine protease in prostatic fluid cleaves the predominant seminal vesicle protein. J Clin Invest 1985;76:1899-903.[Medline]
2 Schellhammer PF, Wright GL Jr. Biomolecular and clinical characteristics of PSA and other candidate prostate tumor markers. Urol Clin North Am 1993;20:597-606.[Medline]
3 Scher HI. Prostate cancer: are we closer to rational treatment selection? Curr Opin Oncol 1992;4:442-54.[Medline]
4 Kelly WK, Scher HI, Mazumdar M, Vlamis V, Schwartz M, Fossa SD. Prostate-specific antigen as a measure of disease outcome in hormone-refractory prostatic cancer. J Clin Oncol 1993;11:607-15.[Abstract]
5 Seidman AD, Scher HI, Petrylak D, Dershaw DD, Curley T. Estramustine and vinblastine: use of prostate specific antigen as a clinical trial end point in hormone refractory prostatic cancer. J Urol 1992;147:931-4.[Medline]
6 Hudes GR, Greenberg R, Krigel RL, Fox S, Scher R, Litwin S, et al. Phase II study of estramustine and vinblastine, two microtubule inhibitors, in hormone-refractory prostate cancer. J Clin Oncol 1992;10:1754-61.[Abstract]
7 Amato RJ, Ellerhurst J, Bui C, Logothetis CJ. Estramustine and vinblastine for patients with progressive androgen-independent adenocarcinoma of the prostate. Urol Oncol 1995;1:168-72.
8 Pienta KJ, Redman B, Hussain M, Cummings G, Esper PS, Appel C, et al. Phase II evaluation of oral estramustine and oral etoposide in hormone-refractory adenocarcinoma of the prostate. J Clin Oncol 1994;12:2005-12.[Abstract]
9 Hudes GR, Nathan FE, Khater C, Greenberg R, Gomella L, Stern C, et al. Paclitaxel plus estramustine in metastatic hormone-refractory prostate cancer. Semin Oncol 1995;22(5 Suppl 12):41-5.[Medline]
10 Sartor O, Cooper M, Weinberger M, Headlee D, Thibault A, Tompkins A, et al. Surprising activity of flutamide withdrawal, when combined with aminoglutethimide, in treatment of "hormone-refractory" prostate cancer [published erratum appears in J Natl Cancer Inst 1994;86:463]. J Natl Cancer Inst 1994;86:222-7.[Abstract]
11 Sridhara R, Eisenberger MA, Sinibaldi VJ, Reyno LM, Egorin MJ. Evaluation of prostate-specific antigen as a surrogate marker for response of hormone-refractory prostate cancer to suramin therapy. J Clin Oncol 1995;13:2944-53.[Abstract]
12 Kelly WK, Scher HI, Mazumdar M, Pfister D, Curley T, Leibertz C, et al. Suramin and hydrocortisone: determining drug efficacy in androgen-independent prostate cancer. J Clin Oncol 1995;13:2214-22.[Abstract]
13 Dimopoulos MA, Panopoulos C, Bamia C, Deliveliotis C, Alivizatos G, Pantazopoulos D, et al. Oral estramustine and oral etoposide for hormone-refractory prostate cancer. Urology 1997;50:754-8.[Medline]
14 Smith DC, Dunn RL, Strawderman MS, Pienta KJ. Change in serum prostate-specific antigen as a marker of response to cytotoxic therapy for hormone-refractory prostate cancer. J Clin Oncol 1998;16:1835-43.[Abstract]
15 Kelly WK, Curley T, Leibretz C, Dnistrian A, Schwartz M, Scher HI. Prospective evaluation of hydrocortisone and suramin in patients with androgen-independent prostate cancer. J Clin Oncol 1995;13:2208-13.[Abstract]
16 Stearns ME, Wang M, Fudge K. Liarozole and 13-cis-retinoic acid anti-prostatic tumor activity [published erratum appears in Cancer Res 1993;53:5831]. Cancer Res 1993;53:3073-7.[Abstract]
17 Peto R, Pike MC, Armitage P, Breslow NE, Cox DR, Howard SV, et al. Design and analysis of randomized trials requiring prolonged observation of each patient. II. Analysis and examples.Br J Cancer 1977;35: 1-39.[Medline]
18 Anderson JR, Cain KC, Gelber RD. Analysis of survival by tumor response. J Clin Oncol 1983;1:710-9.[Abstract]
19 Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958;53:457-81.
20 Anonymous. SAS/STAT user guide. 6th ed. Cary (NC): SAS Institute Inc.; 1990.
21 Cox DR. Regression models and life table (with discussion). JR Stat Soc 1972;B34:187-220.
22 Cox DR. Partial likelihood. Biometrika 1975;62:269-79.
23 Clinical practice guidelines for the use of tumor markers in breast and colorectal cancer. Adopted on May 17, 1996, by the American Society of Clinical Oncology. J Clin Oncol 1996;14:2843-77.[Abstract]
24 Prentice RL. Surrogate endpoints in clinical trials: definition and operational criteria. Stat Med 1989;8:431-40.[Medline]
25 Bander NH. Current status of monoclonal antibodies for imaging and therapy of prostate cancer. Semin Oncol 1994;21:607-12.[Medline]
26 Kobayashi K, Vokes EE, Ratain MJ, Janisch L, Vogelzang NJ. Survival of prostate cancer patients treated with suramin by intermittent infusion without adaptive control: possible impact of prior flutamide therapy on survival [abstract]. Proc ASCO 1996;15:481.
27
Thalmann GN, Sikes RA, Chang SM, Johnston DA, von
Eschenbach AC, Chung LW. Suramin-induced decrease in prostate-specific antigen expression
with no effect on tumor growth in the LNCaP model of human prostate cancer. J Natl
Cancer Inst 1996;88:794-801.
28
Eisenberger MA, Nelson WG. How much can we rely on the
level of prostate-specific antigen as an end point for evaluation of clinical trials? A word of
caution! [editorial]. J Natl Cancer Inst 1996;88:779-81.
29 Eisenberger MA, Crawford ED, McLeod D, et al. A comparison of bilateral orchiectomy with or without flutamide in stage D2 prostate cancer. (NCI INT-0105 SWOG/ECOG). Proc ASCO 1997;16:3.
30 Young CY, Montgomery BT, Andrews PE, Qui SD, Bilhartz DL, Tindall DJ. Hormonal regulation of prostate-specific antigen messenger RNA in human prostatic adenocarcinoma cell line LNCaP. Cancer Res 1991;51:3748-52.[Abstract]
31 Riegman PH, Vlietstra RJ, van der Korput JA, Brinkmann AO, Trapman J. The promoter of the prostate-specific antigen gene contains a functional androgen responsive element. Mol Endocrinol 1991;5:1921-30.[Abstract]
32 Montgomery BT, Young CY, Bilhartz DL, Andrews PE, Prescott JL, Thompson NF, et al. Hormonal regulation of prostate-specific antigen (PSA) glycoprotein in the human prostatic adenocarcinoma cell line, LNCaP. Prostate 1992;21:63-73.[Medline]
33 Wolf DA, Schulz P, Fittler F. Transcription regulation of prostate kallikrein-like genes by androgen. Mol Endocrinol 1992;6:753-62.[Abstract]
34
Scher HI, Mazumdar M, Kelly WK. Clinical trials in relapsed
prostate cancer: defining the target. J Natl Cancer Inst 1996;88:1623-34.
35 Leo ME, Bilhartz DL, Bergstralh EJ, Oesterling JE. Prostate specific antigen in hormonally treated stage D2 prostate cancer: is it always an accurate indicator of disease status? J Urol 1991;145:802-6.[Medline]
36 Gormley GJ, Stoner E, Brushkewitz RC, Imperato-McGinley J, Walsh PC, McConnell JD, et al. The effect of finasteride in men with benign prostatic hyperplasia. The Finasteride Study Group. N Engl J Med 1992;327:1185-91.[Abstract]
37 Andrews PE, Young CY, Montgomery BT, Tindall DJ. Tumor-promoting phorbol ester down-regulates the androgen induction of prostate-specific antigen in a human prostatic adenocarcinoma cell line. Cancer Res 1992;52:1525-9.[Abstract]
38 Peehl DM, Wong ST, Stamey TA. Vitamin A regulates proliferation and differentiation of human prostatic epithelial cells. Prostate 1993;23:69-78.[Medline]
39 Dahiya R, Park HD, Cusick J, Vessella RL, Narayan P. Downregulation of PSA and tumorigenic potential by retinoic acid in prostate cancer cells [abstract]. Proc Am Assoc Cancer Res 1993;34:abstract 1751.
40 Samid D, Shack S, Myers CE. Selective growth arrest and phenotypic reversion of prostate cancer cells in vitro by nontoxic pharmacological concentrations of phenylacetate. J Clin Invest 1993;91:2288-95.[Medline]
41 Melchior S, Stone B, Santucci R, Brown L, True L, Daniel J, et al. Differentiation induces phenylacetate and phenylbutyrate and their effects in vitro and on the advanced prostate cancer (CaP) xenograft model LUCaP 23.1 [abstract]. Proc Am Assoc Cancer Res 1996;36:abstract 498.
42 Hsieh T, Chen SS, Wang X, Wu JM. Regulation of androgen receptor (AR) and prostate specific antigen (PSA) expression in the androgen-responsive human prostate LNCaP cell by ethanolic extracts of the Chinese herbal preparation, PC-SPES. Biochem Mol Biol 1997;42:535-44.
43 Palad AJ, Somers KD, Blackmore PF, Kohn EC, Rhim JS, Wright GL, et al. Sensitivities of human prostate tumor cell lines to the calcium influx inhibitor carboxyamidotriazole (CAI) [abstract]. Proc Am Assoc Cancer Res 1996;36:abstract 1669.
Manuscript received February 12, 1998; revised November 16, 1998; accepted November 27, 1998.
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