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Prognostic Significance of Reverse Transcriptase Polymerase Chain Reaction for Prostate-Specific Antigen in Metastatic Prostate Cancer: A Nested Study Within CALGB 9583

Susan Halabi, Eric J. Small, Daniel F. Hayes, Nicholas J. Vogelzang, Philip W. Kantoff

From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; Breast Oncology Program, University of Michigan; University of Chicago Cancer Research Center and the Section of Hematology and Oncology, Department of Medicine, University of Chicago, Chicago, IL; The Lank Center for Genitourinary Oncology, Department of Medical Oncology Dana-Farber Cancer Institute, Boston, MA.

Address reprint requests to Susan Halabi, PhD, Duke University Medical Center, Box 3958, Durham, NC, 27710, email: susan.halabi{at}duke.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
Purpose: To determine whether reverse transcriptase polymerase chain reaction (RT-PCR) to detect circulating prostate-specific antigen (PSA)-positive cells is a prognostic factor for survival in hormone refractory prostate cancer and to validate the prognostic importance of this test in relation to other known prognostic factors.

Patients and Methods: A single centralized laboratory received and analyzed whole blood for RT-PCR for PSA for a subset of patients enrolled on two Cancer and Leukemia Group B (CALGB) randomized trials (CALGB 9583 and CALGB 9480). Using 9583, a prognostic model was developed and an independent data set (CALGB 9480) was used to validate the fitted model.

Results: Of 162 patients in 9583, 91 (56%) patients were negative for RT-PCR for PSA and 71 (44%) patients were positive. The median survival time was 21 months (95% confidence interval [CI], 18 to 27 months) for RT-PCR-negative patients compared with 11 months (95% CI, 8 to 15 months) for RT-PCR-positive patients (P .001). In multivariable analysis, the hazard ratio (HR) for death was 1.7 (95% CI, 1.2 to 2.4; P = .006) for positive RT-PCR patients compared with negative RT-PCR patients. A fitted model that incorporated RT-PCR for PSA and other factors was used to classify patients from 9480 into one of two risk groups: low or high. We observed good agreement between the observed and predicted survival probabilities for the two risk groups.

Conclusion: RT-PCR to detect PSA-positive circulating cells is confirmed to be a significant prognostic factor of survival in patients with hormone refractory prostate cancer. This model could be used to stratify patients in randomized phase III trials.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
PROSTATE CANCER is the leading cancer among men in the United States, accounting for 31% of all male malignancies. It is estimated that 189,000 men in the United States will be diagnosed with prostate cancer during 2002, and, in the same year, 30,200 men will die from this cancer.¹ Whereas some factors predictive of survival in men with hormone refractory prostate cancer (HRPC) have been identified, these factors generally reflect some combination of host factors (alkaline phosphatase or hemoglobin) and tumor burden.^2,3 Additional markers that directly correlate with survival or clinical progression, especially if they help identify novel targets, are needed.

Several reports indicate the importance of circulating prostate cancer cells as a potential marker of relapse or survival.^4–9 We and others reported that patients being treated with chemotherapy who had no detectable circulating cells as measured by reverse transcriptase polymerase chain reaction (RT-PCR) for prostate-specific antigen (PSA) had a longer survival time than patients in whom circulating cells were detected.^4,5 Further, we reported that RT-PCR for PSA was an independent prognostic factor for survival in men with HRPC.⁴

This study was conducted to confirm this observation prospectively and to test whether it had greater significance in men treated with secondary hormonal therapy for prostate cancer, a subgroup of men who, overall, have better prognosis. Further, we developed and validated a model that incorporated RT-PCR for PSA status as a means to predict overall survival.

	PATIENTS AND METHODS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
Patient Population
Data from two correlative science companion studies that were approved by the Cancer and Leukemia Group B (CALGB) Executive Committee were used in the analysis: CALGB 9583 (learning sample) and CALGB 9480 (validation sample). All patients signed informed consent before study registration to have blood specimens drawn and analyzed. The first study was a randomized phase III study (CALGB 9583) of antiandrogen withdrawal (AAWD) with or without ketoconazole plus hydrocortisone. Between August 1996 and May 2000, 260 patients with metastatic prostate cancer were randomly assigned with equal probability to AAWD (arm 1) or AAWD plus ketoconazole and hydrocortisone (arm 2). Patients on treatment arm 2 received 400 mg of ketoconazole tid and replacement doses of hydrocortisone (40 mg/d). Patients on treatment arm 1 who progressed were crossed over to arm 2 and were treated with 400 mg of ketoconazole and 40 mg of hydrocortisone. Patients were stratified by prior therapy with an antiandrogen (yes or no), type of any androgen deprivation (intermittent or continuous), and initial or delayed use of antiandrogen therapy.

Patients were eligible if they had evidence of progressive metastatic adenocarcinoma of the prostate, a life expectancy of at least 3 months, an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, adequate hematologic reserve, and adequate renal, hepatic, and clotting functions. Patients were allowed one prior hormonal therapy (completed either bicalutamide or flutamide at least 4 weeks before study entry). Patients were not allowed to have received prior chemotherapy, immunotherapy, nonhormonal therapy, or prior therapy with aminoglutethimide, ketoconazole, hydrocortisone, or other corticosteroids.

The independent data set that was used for external validation was CALGB 9480, a study of three different fixed doses of suramin. From February 1996 to July 1998, 390 patients with metastatic HRPC were randomly assigned with equal probability to receive one of three fixed dose regimens of suramin. Random assignment was stratified by site (bone only, soft tissue), performance status (0–1, 2), and number of prior hormonal manipulations (1–2, 3). Additional information on this study was previously described in detail by Kantoff et al.⁴

RNA Isolation and RT-PCR Assay
Details regarding RNA isolation and the RT-PCR assay have been published elsewhere.⁴ Briefly, samples of whole blood were collected from patients and were sent by overnight courier to a central laboratory at the Dana-Farber Cancer Institute for RNA extraction. RNA was isolated from the mononuclear cell fraction by single-step guanadinium thiocyanate extraction.⁴ The RNA was reverse transcribed using a primer previously described and II Rnase H Reverse transcriptase (Gibco BRL, Gaithersburg, MD) to synthesis cDNA for PSA and actin separately.

Statistical Analysis
The main end points were survival duration and progression-free survival (PFS). Survival duration was defined as the time between randomization and death. Patients were censored if they were known to be alive or if they were lost to follow-up. PFS was defined as the time between randomization and disease progression or death, whichever occurred first. For patients with measurable disease, the date of progression was defined as the date of the first computer tomography (CT) scan that demonstrated new lesions showing an increase greater than 25% in the sum of the perpendicular diameters of previously measured disease. For patients with elevated serum PSA (with either evaluable or measurable disease), progressive disease was defined as two consecutive increases in PSA at least 2 weeks apart, each greater than 50% above the nadir or the pretreatment baseline, whichever is lowest. The Kaplan-Meier product-limit estimator was used to estimate the survival probability and the PFS by the RT-PCR for PSA status.¹⁰ The proportional hazards model was used to assess the statistical significance of RT-PCR for PSA for survival in univariable and multivariable analysis.¹¹ The proportional hazards regression method was used to develop a prognostic model using data from CALGB 9583 and to validate the final model. Prognostic factors that were significant at the 0.25 level or less in the univariable analyses were included in the initial multivariable proportional hazards model. Based on ad hoc rules, it is recommended that no more than 20 events should be used for every variable in the multivariable model.¹² Otherwise, the validity of the model may be questionable. In this data set, there were 125 deaths, and the goal was to select a parsimonious model with no more than six factors that would contribute prognostic information. Hazard ratios and 95% confidence intervals (CIs) were computed from the parameter estimate and SE. Baseline PSA, lactate dehydrogenase (LDH), and alkaline phosphatase had skewed distributions and were modeled using the log transformation. Martingale and Schoenfeld residuals were used to check the adequacy of the linearity and the proportional hazards assumptions.¹²

An independent data set (CALGB 9480) was used to validate the final model. For each patient, we calculated a risk score of death using the parameter estimates from the final model that were developed from CALGB 9583. Patients were then ranked in order of increased risk scores and were divided on the basis of the median risk score into two equal risk groups: low or high.¹² We plotted the actual survival (as estimated by the Kaplan-Meier method) against the predicted estimates of survival (based on the proportional hazards model). The means of the continuous covariates and the mode of the binary covariates for patients in each risk group were used in computing the predicted survival probability. The statistical analyses for model development and validation were conducted using S-plus software (Version 3.3, Statistical Sciences, Seattle, WA) and software available in the public domain.¹³ All tests were performed using a two-sided alpha level of 0.05.

	RESULTS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
CALGB 9583: Patient Characteristics
Of the 260 patients enrolled on CALGB 9583, 207 (80%) provided blood at baseline. Of the 207 patients, 30 (14%) patients were actin PCR–negative, indicating the lack of viable cells in the specimen, and 15 patients (7%) did not have sufficient RNA to amplify for RT-PCR. These 45 (21%) specimens were excluded from the analysis as they were considered to be noninformative. The final analysis was based on 162 patients. These patients were followed for a median of 33 months, and their median survival duration was 17 months (95% CI, 14 to 21 months).

Table 1 lists the baseline characteristics of the 162 patients for whom RT-PCR for PSA data were available. The patients included in this study were representative of the entire cohort enrolled on CALGB protocol 9583 (Table 1). The median age was 72, and 82% of these patients were white. Ninety-six percent of the patients had an ECOG performance status of 0 or 1. The majority of patients had metastatic disease; 82% had bone metastases and 31% had lymph node involvement. The median baseline PSA, hemoglobin, and alkaline phosphatase were 59 ng/mL, 13g/dL, and 119 ng/mL, respectively. Of the 162 patients, 91 (56%) patients had undetectable transcripts and 71 (44%) patients had detectable transcripts (seven patients had equivocal results and were categorized with the positive patients).

View larger version (16K): [in this window] [in a new window]   Fig 1. Observed versus predicted survival (based on the model presented in Table 1) for the two risk groups using data from patients enrolled on cancer and leukemia group B (CALGB) 9480.

Multivariable Analysis
In multivariable analysis, the most significant predictor was hemoglobin level (P = .001). RT-PCR for PSA was the second most significant predictor of overall survival adjusting for age, LDH, PSA, performance status, and hemoglobin (Table 3). The HR for patients with positive RT-PCR for PSA was 1.7 (95% CI, 1.2 to 2.4; P = .006) compared with patients with negative RT-PCR. Performance status was another significant factor (P = .009).

We also assessed whether RT-PCR for PSA was a statistically significant predictor of PFS. In the multivariable model, the adjusted HR for RT-PCR for PSA was 1.5 (95% CI, 1.1 to 2.2; P = .034) for patients with detectable transcripts compared with negative RT-PCR patients.

Independent Data Set CALGB 9480
Table 4 presents the baseline characteristics of 156 patients enrolled on CALGB 9480 that were used to validate the final model. Patients who participated in CALGB 9480 had worse prognostic features than patients randomly assigned to CALGB 9583. Patients in CALGB 9480 (suramin trial) were younger and had worse performance statuses and higher PSA and alkaline phosphatase levels than patients enrolled on CALGB 9583. Nonetheless, the median survival duration for the 156 suramin-treated patients was 15 months (95% CI, 13 to 18 months), and the median follow-up for surviving patients was 32 months, similar to the median survival and median follow-up time for the patients in 9583.

0.512 x RT-PCR [coded as zero if negative, otherwise = 1] + 0.481 x performance status [coded as 0 if performance status is 0, otherwise coded as 1] + 0.270 x log (baseline LDH) + 0.097 x log (baseline PSA) + 0.016 x age [as is] - 0.210 x hemoglobin level.

As an example, consider a patient whose age = 70 years, performance status = 2, RT-PCR for PSA test was positive, baseline PSA = 125, baseline LDH = 121, and whose hemoglobin level = 12. The prognostic score for this patient is 0.512 x 1 (positive RT-PCR for PSA test) + 0.481 x 1 (performance status = 1) + 0.097 x 4.828 (log [baseline PSA]) + 0.270 x 4.796 (log [baseline LDH]) + 0.0163 x 70 (age = 70) - 0.210 x 12 (hemoglobin level) = 1.377.

The median risk score among patients who participated in 9583 was 0.80. Applying the cut-point (0.80), we identified two groups of patients in 9480 depending on the risk score. Patients who had a risk score less than or equal to 0.80 were considered to be in the low-risk group, and patients who had a risk score greater than 0.80 were considered to be in the high-risk group. The patient from the example given above with a risk score of 1.377, would be considered to be in the high risk group. The median survival duration was 20 and 12 months for the low- and high-risk groups, respectively. Figure 2 presents the observed (solid line) and predicted (dashed line) survival curves for the two risk groups, low and high. As seen in Fig 2, there was a good agreement between the observed and predicted survival for the two risk groups. For the low-risk group, the observed and predicted survival probabilities were 74% (95% CI, 61% to 83%) and 71% (95% CI, 62% to 79%) at 12 months and 38% (95% CI, 26% to 49%) and 37% (95% CI, 26% to 47%) at 24 months. The observed and predicted survival probabilities for the high risk group were 55% (95% CI, 44% to 64%) and 56% (95% CI, 46% to 65%) at 12 months, and 18% (95% CI, 11% to 26%) and 17% (95% CI, 11% to 26%) at 24 months.

View larger version (16K): [in this window] [in a new window]   Fig 2. Overall survival by reverse transcriptase polymerase chain reaction (RT-PCR) for prostate-specific antigen (PSA) for 162 patients with RT-PCR data enrolled on cancer and leukemia group B (CALGB) 9583.

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

Several reports have shown the importance of circulating prostate cancer cells as a biomarker of relapse or survival in patients with localized disease or advanced disease.^4–9 In this prospective study, the proportion of patients with detectable transcripts was 44%, which is comparable to that previously reported for CALGB 9480.⁴ Using a different assay, Ghossein et al,¹⁴ found the proportion of RT-PCR positivity to be 35% in HRPC patients.

We have constructed a prognostic model that incorporated prognostic factors that not only reflect overall tumor volume and host condition but also a biologic marker of circulating cells. Other important predictors were PSA, hemoglobin, and performance status. Although none of these factors was significant, they contributed prognostic information in predicting survival duration. The final model was validated using an independent data set. The predicted survival estimates at 12 and 24 months were close to the observed survival probability.

Currently available therapies for HRPC patients are palliative and have not been shown to prolong survival duration.^15,16 Identification of prognostic factors is important for classification of patients into different strata.^3,17 By stratifying patients, we are ensuring that the treatment groups are balanced with respect to the known or possible prognostic factors to avoid the possibility of confounding.¹⁷ Further, identification of prognostic factors is important because it can help identify groups of patients who may have a prolonged survival duration or who may partially benefit from therapy.³ Indeed, it is possible that some treatment may be beneficial for only certain subgroups of patients and not for others.

With the exception of the RT-PCR test, the variables included in the model are collected routinely in clinical trials. Because RT-PCR for PSA is a sensitive and validated assay and is not expensive to perform, investigators are encouraged to collect whole blood to determine the RT-PCR for PSA status for patients so that a risk score can be calculated. The prognostic score could be used to stratify patients in future randomized phase III studies.

	APPENDIX

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
The following institutions participated in this study:

CALGB Statistical Office, Durham, NC—Stephen George, PhD; supported by CA33601

Baptist Cancer Institute CCOP, Memphis, TN—Lee S. Schwartzberg, MD; supported by CA71323

Christiana Care Health Services, Inc. CCOP, Wilmington, DE—Irving M. Berkowitz, DO; supported by CA45418

Community Hospital–Syracuse CCOP, Syracuse, NY—Jeffrey Kirshner, MD; supported by CA45389

Dana-Farber Cancer Institute, Boston, MA—George P. Canellos, MD; supported by CA32291

Dartmouth Medical School–Norris Cotton Cancer Center, Lebanon, NH—Marc S. Ernstoff, MD; supported by CA04326

Duke University Medical Center, Durham, NC—Jeffrey Crawford, MD; supported by CA47577

Georgetown University Medical Center, Washington, DC—Edward P. Gelmann, MD, supported by CA77597

Green Mountain Oncology Group CCOP, Bennington, VT—H. James Wallace Jr., MD; supported by CA35091

Kaiser Permanente CCOP, San Diego, CA—Jonathan A. Polikoff, MD; supported by CA45374

Long Island Jewish Medical Center, Lake Success, NY—Marc Citron, MD; supported by CA11028

Massachusetts General Hospital, Boston, MA—Michael L. Grossbard, MD; supported by CA12449

Mount Sinai School of Medicine, New York, NY—Lewis R. Silverman, MD; supported by CA04457

Rhode Island Hospital, Providence, RI—Louis A. Leone, MD; supported by CA08025

Roswell Park Cancer Institute, Buffalo, NY—Ellis Levine, MD; supported by CA02599

Southeast Cancer Control Consortium Inc. CCOP, Goldsboro, NC—James N. Atkins, MD; supported by CA45808

Southern Nevada Cancer Research Foundation CCOP, Las Vegas, NV—John Ellerton, MD; supported by CA35421

SUNY Upstate Medical University, Syracuse, NY—Stephen L. Graziano, MD; supported by CA21060

The Ohio State University Medical Center, Columbus, OH—Clara D Bloomfield, MD; supported by CA77658

University of Alabama Birmingham, Birmingham, AL—Robert Diasio, MD; supported by CA47545

University of California at San Diego, San Diego, CA—Stephen L Seagren, MD; supported by CA11789

University of California at San Francisco, San Francisco, CA—Alan P. Venook, MD; supported by CA60138

University of Chicago Medical Center, Chicago, IL—Gini Fleming, MD; supported by CA41287

University of Illinois MBCCOP, Chicago, IL—Jeffrey A. Sosman, MD; supported by CA74811

University of Maryland Cancer Center, Baltimore, MD—David Van Echo, MD; supported by CA31983

University of Minnesota, Minneapolis, MN—Bruce A. Peterson, MD; supported by CA16450

University of Missouri/Ellis Fischel Cancer Center, Columbia, MO—Michael C. Perry, MD; supported by CA12046

University of Nebraska Medical Center, Omaha, NE—Anne Kessinger, MD; supported by CA77298

University of North Carolina at Chapel Hill, Chapel Hill, NC—Thomas C. Shea, MD; supported by CA47559

University of Tennessee Memphis, Memphis, TN—Harvey B. Niell, MD; supported by CA47555

Vermont Cancer Center, Burlington, VT—Hyman B. Muss, MD; supported by CA77406

Wake Forest University School of Medicine, Winston-Salem, NC—David D. Hurd, MD; supported by CA03927

Washington University School of Medicine, St. Louis, MO—Nancy Bartlett, MD; supported by CA77440

Walter Reed Army Medical Center, Washington, DC—John C. Byrd, MD; supported by CA26806

Weill Medical College of Cornell University, New York, NY—Michael Schuster, MD; supported by CA07968

	ACKNOWLEDGMENTS

 
Research for CALGB 9480 and 9583 was supported, in part, by grants from the National Cancer Institute (CA31946 and CA36601) to the Cancer and Leukemia Group B. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute. We express our appreciation to Ellen Kaplan and Cary Werner for their help in the statistical analysis.

	NOTES

 
Supported in part by grants from the National Cancer Institute (CA31946 and CA36601), CCSG P30 CA14599, and Fashion Footwear Foundation of New York and Shoes-on-Sale/QVC Presents.

	REFERENCES

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
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Submitted April 16, 2002; accepted October 9, 2002.

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