This Article Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by D’Amico, A. V. Search for Related Content PubMed PubMed Citation Articles by D’Amico, A. V.

Predicting Prostate-Specific Antigen Recurrence Established: Now, Who Will Survive?

Brigham and Women’s Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA

THE GOALS OF predicting outcome after definitive therapy based on pretreatment clinical factors for patients with clinically localized prostate cancer are two-fold. First, these data could be used to counsel an individual patient regarding the likelihood of success following a given therapeutic approach, providing the basis on which to select a treatment. Second, patients whose cancer control rates have been found to be unacceptably low with currently available treatments could elect to enroll on a randomized clinical trial whose purpose would be to investigate whether a new therapy improves cancer control.

Alan Partin, MD, from Johns Hopkins University, initiated outcome prediction after definitive therapy based on pretreatment parameters. The Partin tables, which provided a prediction of pathologic findings at radical prostatectomy (RP), were first published in 1993¹ and were based on data from 703 patients. These tables were later updated using a multi-institutional database of 4,133 patients² and again updated using a contemporary population consisting of 5,079 men treated at Johns Hopkins University³ in 2001. Thus, in 1993, the basis for predicting organ-confined disease was established, which was thought to be a necessary and possibly sufficient step for cure after RP. Since that time, several investigators^4,5 have shown that, despite organ-confined and margin-negative disease, patients can still sustain postoperative prostate-specific antigen (PSA) failure.

Therefore, attention was next drawn to the prediction of PSA failure after definitive therapy as the next necessary step toward predicting high risk of death due to prostate cancer. To that end, Kattan,⁶ Snow,⁷ Graefen,⁸ Duchesne,⁹ Pisansky,¹⁰ Shipley,¹¹ Zagars,¹² Ragde,¹³ Leibel,¹⁴ Bauer,¹⁵ Potter,¹⁶ Moul,¹⁷ Grossfeld,¹⁸ Roach,¹⁹ Movsas,²⁰ and I²¹ and colleagues have developed algorithms based on pretreatment clinical parameters to predict the time to posttherapy PSA failure after RP, external-beam radiation therapy (RT), or prostate brachytherapy. Although the definitions of PSA failure, minimum and median follow-up, sample sizes, and the use of sextant versus extended biopsies varied among these studies, a few clear messages emerged. The risk of PSA failure after RP or any form of RT depended on the pretreatment PSA level and Gleason score for patients with clinically localized disease and, to a lesser extent, the clinical stage based on the digital rectal examination.^6-21 In addition, the percentage of positive biopsy specimens also seemed to add predictive information regarding PSA outcome for patients in whom PSA and Gleason score did not provide a clear risk assessment of PSA outcome into low- or high-risk categories.^21-23

In this issue of the Journal of Clinical Oncology, Graefen et al²⁴ have provided further evidence to validate Kattan’s pretreatment model⁶ using data from an international contingent. Specifically, they examined a group of 6,232 men at seven institutions with T1a to T3a prostate cancer who underwent RP. They found that the postoperative PSA failure rates of the validation cohort were accurately predicted using the Kattan pretreatment model.⁶ They also acknowledge the limitations of the study, which included 1,524 patients who did not have the primary and secondary Gleason grade that is normally used to calculate PSA outcome in the Kattan model. In addition, postoperative radiation therapy was used in 8% of the entire validation cohort and in 28% of patients from one institution, which could also have an impact on the PSA outcome data in the validation cohort. Nevertheless, despite these limitations, the receiver operating characteristic analysis yielded values for the area under the curve²⁵ that ranged from 0.67 to 0.83, providing support for the validation of the preoperative model of Kattan.⁶

However, the study raised two important questions that have subsequently been answered in part by the authors. First, could other models also be validated using their data set? Well, in fact, yes. On May 28, 2002, at the 100th Annual Meeting of the American Urological Association, Graefen et al presented the same receiver operating characteristic analysis²⁶ used in the current study to show that both the Kattan nomogram⁶ and the pretreatment risk groups defined by D’Amico et al²¹ provided similar accuracy in predicting PSA failure after RP with area-under-the-curve values of 0.81 and 0.80, respectively, when applied to 932 men treated for prostate cancer in Hamburg, Germany.²⁶ Therefore, more than a single method exists to accurately predict posttreatment PSA failure. Second, and more importantly, the clinical value to the patient and treating physician of any pretreatment predictive model, including those by Kattan et al⁶ and D’Amico et al,²¹ will ultimately be measured based on its ability to accurately predict prostate cancer-specific death (PCSD). Therefore, the clinically relevant question remains: Do any of these models accomplish that ultimate goal? Possibly.

To identify those patients in whom PSA failure is likely to translate into death from prostate cancer, investigators have studied the predictors of the time to documentation of distant failure (ie, positive bone scan) after PSA failure. From these investigations,^27-32 one posttreatment clinical parameter, the posttreatment PSA doubling time (DT), has emerged from both the surgical^27-29 and radiation^30-32 literature as a potential candidate for predicting the likelihood of prostate cancer death after primary treatment failure. In particular, several reports of patients treated with surgery^27-29 and radiation^30-32 suggest that a rapid posttreatment PSA DT (6 to 12 months) is a significant predictor of time to distant failure after PSA failure. In addition, one of the radiation studies by Sandler et al³² also found the posttreatment PSA DT to be predictive of time to PCSD. Specifically, they estimated the 5-year PCSD to be 52% versus 10% (P = .007) for patients with a post-RT PSA DT of approximately 1 year or less compared with greater than 1 year, respectively. Data from our institution have shown that patients with a posttreatment PSA DT of less than 1 year have nearly equal estimates of 5-year PCSD (52%) and 5-year all-cause death (53%). When considered in the context of the results of the prior studies,^27-32 this additional finding provides evidence to support a posttreatment PSA DT of less than 1 year as both a powerful predictor of and candidate surrogate for PCSD after conventional-dose RT. Other investigators are currently performing independent assessments of this finding.

Therefore, I congratulate Graefen et al on validating two pretreatment models^6,21 for predicting PSA outcome after definitive therapy. Using either the Kattan nomogram⁶ or the risk groups defined by D’Amico et al²¹ permits the identification of patients at low and high risk of PSA failure after definitive local therapy. Patients at high risk for PSA failure could elect to participate in clinical trials that are exploring whether the addition of novel systemic therapies to local therapy will improve outcome. Conversely, patients at low risk for PSA failure can be reassured that local therapy alone is likely to lead to long-term cancer control. In time, I expect that the authors will be instrumental in validating other pretreatment models that can predict PSA outcome and ultimately may help the field to validate a pretreatment model that can predict the clinically important end point of PCSD.

REFERENCES

1. Partin AW, Yoo J, Carter HB, et al: The use of prostate specific antigen, clinical stage and Gleason score to predict pathological stage in men with localized prostate cancer. J Urol 150: 110-114, 1993[Medline]

2. Partin AW, Kattan MW, Subong EN, et al: Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer: A multi-institutional update. JAMA 277: 1445-1551, 1997[Abstract]

3. Partin AW, Mangold LA, Lamm DM, et al: Contemporary update of prostate cancer staging nomograms (Partin tables) for the new millennium. Urology 58: 843-848, 2001[CrossRef][Medline]

4. Lerner SE, Blute ML, Bergstralh EJ, et al: Analysis of risk factors for progression in patients with pathologically organ confined prostate cancers after radical retropubic prostatectomy. J Urol 156: 137-143, 1996[CrossRef][Medline]

5. D’Amico AV, Whittington R, Malkowicz SB, et al: PSA failure despite pathologic organ confined disease: The basis for an adjuvant therapy trial. J Clin Oncol 15: 1465-1469, 1997[Abstract]

6. Kattan MW, Eastham JA, Stapleton AM, et al: A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst 90: 766-771, 1998[Abstract/Free Full Text]

7. Snow PB, Smith DS, Catalona WJ: Artificial neural networks in the diagnosis and prognosis of prostate cancer: A pilot study. J Urol 152: 1923-1926, 1994[Medline]

8. Graefen M, Noldus J, Pichlmeier U, et al: Early prostate-specific antigen relapse after radical retropubic prostatectomy: Prediction on the basis of preoperative and postoperative tumor characteristics. Eur Urol 36: 21-30, 1999[Medline]

9. Duchesne GM, Bloomfield D, Wall P: Identification of intermediate-risk prostate cancer patients treated with radiotherapy suitable for neoadjuvant hormone studies. Radiother Oncol 38: 7-12, 1996[CrossRef][Medline]

10. Pisansky TM, Kahn MJ, Rasp GM, et al: A multiple prognostic index predictive of disease outcome after irradiation for clinically localized prostate cancer. Cancer 79: 337-344, 1997[CrossRef][Medline]

11. Shipley WU, Thames HD, Sandler HM, et al: The results of radical radiation therapy for men with clinically localized prostate cancer: An ASTRO sponsored multi-institutional pooled analysis. JAMA 281: 1597-1603, 1999

12. Zagars GK, Pollack A, Kavadi VS, et al: Prostate specific antigen and radiation therapy for clinically localized prostate cancer. Int J Radiat Oncol Biol Phys 32: 293-306, 1995[Medline]

13. Ragde H, Blasko JC, Grimm PD, et al: Interstitial iodine-125 radiation without adjuvant therapy in the treatment of clinically localized prostate carcinoma. Cancer 80: 442-453, 1997[CrossRef][Medline]

14. Leibel SA, Zelefsky MJ, Kutcher GJ, et al: The biologic basis and clinical application of three-dimensional conformal external beam radiation therapy in carcinoma of the prostate. Semin Oncol 21: 580-597, 1994[Medline]

15. Bauer JJ, Connelly RR, Sesterhenn IA, et al: Biostatistical modeling using traditional variables and genetic biomarkers for predicting the risk of prostate carcinoma recurrence after radical prostatectomy. Cancer 79: 952-957, 1997[CrossRef][Medline]

16. Potter SR, Miller MC, Mangold LA, et al: Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy. Urology 54: 791-796, 1999[CrossRef][Medline]

17. Moul JW, Connelly RR, Lubeck DP, et al: Predicting risk of prostate specific antigen recurrence after radical prostatectomy with the Center for Prostate Disease Research and Cancer of the Prostate Strategic Urologic Research Endeavor databases. J Urol 166: 1322-1327, 2001[CrossRef][Medline]

18. Grossfeld GD, Chang JJ, Broering JM, et al: Under staging and under grading in a contemporary series of patients undergoing radical prostatectomy: Results from the Cancer of the Prostate Strategic Urologic Research Endeavor database. J Urol 165: 851-856, 2001[CrossRef][Medline]

19. Roach M, Lu J, Pilepich MV, et al: Four prognostic groups predicting long term survival from prostate cancer following radiotherapy alone in RTOG clinical trials. Int J Radiat Oncol Biol Phys 47: 609-615, 2000[CrossRef][Medline]

20. Movsas B, Hanlon A, Teshima T, et al: Analyzing predictive models following definitive radiotherapy for prostate cancer. Cancer 80: 1093-2102, 1997[CrossRef][Medline]

21. D’Amico AV, Whittington R, Malkowicz SB, et al: The clinical utility of the percent of positive prostate biopsies in defining biochemical outcome following radical prostatectomy for patients with clinically localized prostate cancer. J Clin Oncol 18: 1164-1172, 2000[Abstract/Free Full Text]

22. Grossfeld GD, Latini DM, Lubeck DP, et al: Predicting disease recurrence in intermediate and high-risk patients undergoing radical prostatectomy using percent positive biopsies: Results from CaPSURE. Urology 59: 560-565, 2002[CrossRef][Medline]

23. D’Amico AV, Schultz D, Silver B, et al: The clinical utility of the percent of positive prostate biopsies in predicting biochemical outcome following external beam radiation therapy for patients with clinically localized prostate cancer. Int J Radiat Oncol Biol Phys 49: 679-684, 2001[CrossRef][Medline]

24. Graefen M, Karakiewicz PI, Cagiannos I, et al: International validation of a preoperative nomogram for prostate cancer recurrence after radical prostatectomy. J Clin Oncol 20: 3206-3212, 2002[Abstract/Free Full Text]

25. Harrell FE Jr, Califf RM, Pryor DB, et al: Evaluating the yield of medical tests. JAMA 247: 2543-2546, 1982[Abstract]

26. Graefen M, Karakiewicz PI, Cagiannos I, et al: A comparison of two preoperative nomograms predicting recurrence following radical prostatectomy. J Urol 167: 231, 2002 (abstr 919.11)

27. Pound CR, Partin AW, Eisenberger MA, et al: Natural history of progression after PSA elevation following radical prostatectomy. JAMA 281: 1591-1596, 1999[Abstract/Free Full Text]

28. Patel A, Dorey F, Franklin J, et al: Recurrence patterns after radical retropubic prostatectomy: Clinical usefulness of prostate specific antigen doubling times and log slope prostate specific antigen. J Urol 158: 1441-1445, 1997[CrossRef][Medline]

29. Roberts SG, Blute ML, Bergstralh EJ, et al: PSA doubling time as a predictor of clinical progression after biochemical failure following radical prostatectomy for prostate cancer. Mayo Clin Proc 76: 576-581, 2001[Medline]

30. Lee WR, Hanks GE, Hanlon A: Increasing prostate-specific antigen profile following definitive radiation therapy for localized prostate cancer: Clinical observations. J Clin Oncol 15: 230-238, 1997[Abstract/Free Full Text]

31. Sartor CI, Strawderman MH, Lin XH, et al: Rate of PSA rise predicts metastatic versus local recurrence after definitive radiotherapy. Int J Radiat Oncol Biol Phys 38: 941-947, 1997[CrossRef][Medline]

32. Sandler HM, Dunn RL, McLaughlin PW, et al: Overall survival after prostate-specific-antigen detected recurrence following conformal radiation therapy. Int J Radiat Oncol Biol Phys 48: 629-623, 2000[CrossRef][Medline]

This article has been cited by other articles:

				M.-E. Taplin and P. W. Kantoff The Ingredients for Prostate Cancer Nomograms: The Addition of Biomarkers Sets the Table for Future Recipes J. Clin. Oncol., October 1, 2003; 21(19): 3552 - 3553. [Full Text] [PDF]

				A. V. D'Amico, J. W. Moul, P. R. Carroll, L. Sun, D. Lubeck, and M.-H. Chen Surrogate End Point for Prostate Cancer-Specific Mortality After Radical Prostatectomy or Radiation Therapy J Natl Cancer Inst, September 17, 2003; 95(18): 1376 - 1383. [Abstract] [Full Text] [PDF]

This Article Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a colleague Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Save to my personal folders Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by D’Amico, A. V. Search for Related Content PubMed PubMed Citation Articles by D’Amico, A. V.