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Clinical Cancer Research 14, 3785, June 15, 2008. doi: 10.1158/1078-0432.CCR-07-4969
© 2008 American Association for Cancer Research
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Imaging, Diagnosis, Prognosis

Improved Prediction of Disease Relapse after Radical Prostatectomy through a Panel of Preoperative Blood-Based Biomarkers

Shahrokh F. Shariat1, Jose A. Karam1, Jochen Walz2,3, Claus G. Roehrborn1, Francesco Montorsi4, Vitaly Margulis1, Fred Saad2, Kevin M. Slawin5 and Pierre I. Karakiewicz2

Authors' Affiliations: 1 Department of Urology, The University of Texas Southwestern Medical Center, Dallas, Texas; 2 Cancer Prognostics and Health Outcomes Unit, University of Montreal, Montreal, Quebec, Canada; 3 Department of Urology, University Medical Centre Eppendorf, Hamburg, Germany; 4 Department of Urology, Vita-Salute University, Milan, Italy; and 5 Scott Department of Urology, Baylor College of Medicine, Houston, Texas

Requests for reprints: Shahrokh F. Shariat, Department of Urology, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390-9110. Phone: 469-363-8500; Fax: 214-648-8786; E-mail: Shahrokh.Shariat{at}UTSouthwestern.edu.


   Abstract
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 Abstract
Materials and Methods
 Results
 Discussion
 Disclosure of Potential...
 References
 
Purpose: The preoperative blood levels of biomarkers may allow accurate identification of patients who are likely to fail radical prostatectomy as a first-line therapy for localized prostate cancer, thereby allowing more efficient delivery of neoadjuvant and adjuvant therapy. The aim of this study was to determine the added value of biomarkers relative to established predictors of biochemical recurrence, such as clinical stage, biopsy Gleason sum, and preoperative prostate-specific antigen.

Experimental Design: The preoperative plasma levels of transforming growth factor-β1 (TGF-β1), interleukin-6 (IL-6), soluble IL-6 receptor (sIL-6R), vascular endothelial growth factor (VEGF), vascular cell adhesion molecule-1 (VCAM-1), endoglin, urokinase-type plasminogen activator (uPA), plasminogen activator inhibitor-1, and uPA receptor were measured with the use of commercially available enzyme immunoassays in 423 consecutive patients treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostate cancer. Multivariable models were used to explore the gain in the predictive accuracy of the models. This predictive accuracy was quantified by the concordance index statistic and was validated with 200 bootstrap resamples.

Results: In standard multivariable analyses, TGF-β1 (P < 0.001), sIL-6R (P < 0.001), IL-6 (P < 0.001), VCAM-1 (P < 0.001), VEGF (P = 0.008), endoglin (P = 0.002), and uPA (P < 0.001) were associated with biochemical recurrence. The multivariable model containing standard clinical variables alone had an accuracy of 71.6%. The addition of TGF-β1, sIL-6R, IL-6, VCAM-1, VEGF, endoglin, and uPA increased the predictive accuracy by 15% to 86.6% (P < 0.001) and showed excellent calibration.

Conclusions: A nomogram based on these biomarkers improves the accuracy of standard predictive models and could help counsel patients about their risk of biochemical recurrence following radical prostatectomy.

Prostate cancer is the most commonly diagnosed cancer and the second leading cause of cancer death in men in the United States (1). Whereas local therapies with curative intent, such as radical prostatectomy and radiotherapy, may result in durable disease control, up to 30% of patients experience biochemical recurrence (2). In these patients, biochemical recurrence is often due to the early dissemination of microscopic metastatic disease that remains undetected by standard pretreatment staging modalities. The identification of patients who are likely to fail definitive local therapy would be helpful in selecting patients best suited for local adjuvant radiation therapy or clinical trials of early systemic intervention (3). Predictive tools based on standard clinicopathologic variables, such as Kattan's preoperative nomogram, predict biochemical recurrence with ~74% to 79% accuracy (4, 5). These models are not perfect, and the addition of other clinical variables has not improved the predictive accuracy of these models (6, 7). This is largely due to the heterogeneous biological behavior of tumors with the same histopathologic features. Biomarkers may help refine clinical decision-making. However, biomarkers capable of improving the accuracy of established predictors of cancer recurrence have not yet been identified. In addition, the emergence of new therapeutic approaches for prostate cancer cannot flourish without a set of markers to serve as prognosticators and/or therapeutic targets.

We and others have previously shown that preoperative blood levels of transforming growth factor-β1 (TGF-β1), interleukin-6 (IL-6), soluble IL-6 receptor (sIL-6R), vascular endothelial growth factor (VEGF), vascular cell adhesion molecule-1 (VCAM-1 or CD106), endoglin, urokinase-type plasminogen activator (uPA), plasminogen activator inhibitor-1, and uPA receptor (uPAR) are associated with biologically aggressive prostate cancer and eventual disease progression (8–12). However, the question of whether one or several of these biomarkers can improve the ability of established pretreatment predictors of cancer recurrence remains unanswered. This question requires more than the conventional univariable and multivariable analyses with associated hazard rates and P values. For biomarkers to be clinically useful, they must add unique predictive information, thus improving the performance of a nomogram constructed without the new biomarker by a significant margin (13). Therefore, we assessed the ability of promising biomarkers to improve the accuracy of variables that represent the established ingredients of the most widely used preoperative prediction tool in prostate cancer, namely the Kattan preoperative nomogram predicting biochemical recurrence after radical prostatectomy (14, 15).


   Materials and Methods
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 Materials and Methods
Results
 Discussion
 Disclosure of Potential...
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Patient population. This study received institutional review board approval and was conducted according to Health Insurance Portability and Accountability Act guidelines. We measured the preoperative plasma levels of TGF-β1, sIL-6R, IL-6, VCAM-1, VEGF, endoglin, PAI-1, uPA, and uPAR in 423 patients treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostatic adenocarcinoma between July 1994 and November 1997. No patient received neoadjuvant chemotherapy, hormone therapy, or radiotherapy, and none had secondary cancers. Dedicated genitourinary pathologists who were blinded to clinical outcome examined all prostatectomy specimens in accordance with the guidelines of the College of American Pathologists (16).

The patients were followed every 3 months for the 1st year, semiannually from the 2nd through the 5th year, and annually thereafter with digital rectal examinations and serum prostate-specific antigen measurements. Biochemical recurrence was defined as a sustained elevation, on two or more occasions, of serum total prostate-specific antigen >0.2 ng/mL and was backdated to the first value >0.2 ng/mL. No patient received adjuvant therapy before BCR.

Biomarker measurements. Preoperative plasma samples were collected at least 4 wk after transrectal guided needle biopsy of the prostate, typically on the morning of the day of surgery after an overnight fast. Blood was collected into Vacutainer CPT tubes (Becton Dickinson) containing sodium citrate anticoagulant and centrifuged at room temperature for 20 min at 1,500 x g. The top layer, corresponding to diluted plasma, was decanted with the use of sterile transfer pipettes and immediately frozen and stored at –80°C in polypropylene cryopreservation vials (Nalge Nunc).

Detailed biomarker collection and measurement protocols were described elsewhere (8–12). Briefly, serum total prostate-specific antigen was measured with the Hybritech assay (Hybritech, Inc.). Plasma TGF-β1, IL-6, sIL-6R, VEGF, VCAM-1, and endoglin were measured with commercially available enzyme immunoassays from R&D Systems. Plasma uPA, PAI-1, and uPAR levels were measured with enzyme immunoassays from American Diagnostica. We previously found that the levels of some of the biomarkers were significantly higher when measured in serum than when measured in plasma (9, 10, 12). Because some of the biomarkers are present in platelet granules and are released upon platelet activation, the higher levels of these biomarkers in serum were likely due, at least in part, to release from damaged platelets, making the quantification of non–platelet-derived biomarker levels less accurate. Therefore, before assessment, an additional centrifugation step of the plasma was done at 10,000 x g for 10 min at room temperature for complete platelet removal. Every sample was run in duplicate, and the mean was used. The differences between the two measurements for the biomarkers were minimal (intra-assay precision coefficients of variation <10%).

Statistical analysis. Multivariable Cox proportional hazards regression analysis addressed the time to biochemical recurrence and relied on the established clinical predictors, such as preoperative serum prostate-specific antigen, clinical stage, and biopsy Gleason sum. The accuracy of individual variables and of multivariable models was tested with the use of Harrell's concordance index, which approximates the area under the curve in censored data (17). To assess the effect of adding the biomarkers to the base predictive model, additional models were fitted with the use of the clinical predictors as well as one or several biomarkers. Stepwise backward variable selection through Akaike's criteria was used to exclude variables with limited prognostic ability from the full model. All univariable and multivariable models were internally validated with 200 bootstrap resamples (18, 19).

The regression coefficients of the multivariable Cox regression model were used to generate a prognostic nomogram. The performance characteristics of the nomogram were explored graphically with the calibration plot, which graphs the relationship between nomogram predicted probabilities of biochemical recurrence and the observed rate of biochemical recurrence. Predictive accuracy estimates were compared with the Mantel-Haenszel test. Statistical analyses were done with commercially available software (S-PLUS 2000 Professional, MathSoft, Inc.) with additional functions (design and Hmisc) added (20). All P values were two sided, and the significance level was set at 0.05.


   Results
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 Materials and Methods
 Results
Discussion
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The patient characteristics are shown in Table 1 . Biochemical recurrence was observed in 75 patients (17.7%). For patients without biochemical recurrence, the median follow-up was 47.3 months (maximum 103), and 31.3% (n = 132) had a follow-up longer than 60 months. Biochemical recurrence-free survival probability at 5 years after surgery was 77% (95% confidence interval, 72.0-82.0%). Table 2 shows the univariable Cox regression analyses and the concordance index of these models. Of the biomarkers investigated, TGF-β1 (P < 0.001), sIL-6R (P < 0.001), IL-6 (P < 0.001), VCAM-1 (P < 0.001), VEGF (P = 0.002), endoglin (P < 0.001), uPA (P < 0.001), and uPAR (P < 0.001) were associated with biochemical recurrence.


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  		Table 1. Descriptive table for 423 consecutive men treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostate cancer

 

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  		Table 2. Univariable Cox regression analysis addressing biochemical recurrence at 5 y for 423 consecutive men treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostate cancer

 
Table 3 shows multivariable Cox regression models. The multivariable model containing standard clinical variables alone (the base model) had an accuracy of 71.6%. The addition of TGF-β1 increased the accuracy to 81.9%. The addition of either sIL-6R or uPA resulted in 77.0% and 74.2% accuracy, respectively. When all biomarkers were added, TGF-β1 (P < 0.001), sIL-6R (P < 0.001), IL-6 (P < 0.001), VCAM-1 (P < 0.001), VEGF (P = 0.008), endoglin (P = 0.002), and uPA (P < 0.001) were independent predictors of biochemical recurrence. The full model combining all biomarkers had an accuracy of 86.2%, which was statistically significantly different from that of the clinical model (P < 0.001). Within that model, PAI-1 and uPAR did not improve accuracy, and their removal resulted in 86.6% accuracy. Figure 1A shows the preoperative nomogram predicting the probability of biochemical recurrence–free survival 5 years after radical prostatectomy. This model includes the standard clinical predictors and the biomarkers. Within the nomogram, influential biomarkers, such as VCAM1, TGF-β1, and sIL-6R contributed more risk points than the most adverse clinical feature (i.e., prostate-specific antigen). The calibration plot of the model (Fig. 1B) indicates excellent balance between the predicted and observed biochemical recurrence rates.


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  		Table 3. Multivariable Cox regression analysis addressing biochemical recurrence at 5 y for 423 consecutive men treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostate cancer

 

Figure 1
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  		Figure 1. A, nomogram predicting biochemical recurrence-free survival at 5 y for 423 consecutive men treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostate cancer. B, calibration plot of the nomogram.

 

   Discussion
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 Materials and Methods
 Results
 Discussion
Disclosure of Potential...
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Patients treated with radical prostatectomy remain at risk of biochemical recurrence for many years, with as many as 30% experiencing biochemical recurrence within 5 years of surgery (2). The identification of patients at increased risk for biochemical recurrence would allow the administration of neoadjuvant and adjuvant therapy and/or inclusion in clinical trials of systemic therapy (3). Therefore, the accurate prediction of the probability of biochemical recurrence is important for the management of patients with prostate cancer. The predictive accuracy of the most widely used preoperative model for prediction of biochemical recurrence after radical prostatectomy ranges from 74% to 79% (4, 5). The emergence of new therapeutic approaches cannot flourish without a more reliable set of markers to serve as prognosticators, targets, and/or surrogate end points of disease progression and response to therapy. There is no doubt that we are approaching a time when the use of proper biomarkers will help detect, monitor, and manage the progression of the disease as well as assist with therapeutic decisions.

Over the past two decades, the molecular dissection of carcinogenesis has increased our understanding of the pathways that are altered in neoplastic cells. The protein expression profiling of prostate cancer offers an alternative means to distinguish aggressive tumor biology and may improve the accuracy of outcome prediction for patients treated with radical prostatectomy. Because the most useful clinical biomarkers will likely be those that can be assayed from blood, there is much interest in profiling blood proteins. Indeed, several blood-based biomarkers have been shown to be associated with the biological behavior of prostate cancer after adjusting for the effects of standard clinicopathologic features. However, despite the increasing number of published biomarker studies, the current status of translational research, with few exceptions, has not yet allowed physicians and patients to fully benefit from these discoveries in clinical practice, such as individualized, evidence-based recommendations.

We tested the predictive ability of a panel of several promising biomarkers and found that the addition of the preoperative plasma levels of these markers (i.e., TGF-β1, sIL-6R, IL-6, VCAM-1, VEGF, endoglin, and uPA) improves the predictive accuracy by 15.0% from 71.6% to 86.6%. This increase substantially exceeds accuracy gains obtained from the consideration of detailed pathologic descriptors of prostate cancer at radical prostatectomy or gains described in the single previous report of biomarker-based predictions of biochemical recurrence after radical prostatectomy (21); for example, the consideration of tumor volume and high-grade tumor volume measured by labor-intensive computer planimetry increased accuracy by 1.3% (22). Such a nomogram could constitute the new standard for counseling patients about their risk of biochemical recurrence following surgery and for designing clinical trials to test neoadjuvant and/or adjuvant treatment strategies in high-risk patients.

The choice of the biomarkers in the current study was based on preclinical and clinical investigation of biological pathways thought to be important in pathogenesis and progression of prostate cancer. Elevated plasma levels of TGF-β1 in patients with clinically evident or occult metastatic prostate cancer, for example, seem to result either from direct production from foci of metastatic tumor or in reaction to it as a host's response to cancer invasion and dissemination and not necessarily as the result of production by the primary tumor (10). We and others have shown that increased local expression of TGF-β1 is associated with higher tumor grade, tumor invasion, and metastatic progression in patients with prostate cancer(23, 24).

Similarly, elevated circulating levels of IL-6 and sIL-6R have been associated with features of aggressive prostate cancer (i.e., greater prostatic tumor volume and higher final Gleason sum), advanced disease stage, presence of distant metastases and metastasis-related morbidity, overall and aggressive disease progression, and decreased survival (8, 10, 25, 26). However, in contrast to TGF-β1, elevated circulating levels of IL-6 and sIL-6R are produced primarily by tumor cells in the primary prostate cancer, and circulating levels of IL-6 and sIL-6R seem to be associated only with the potential of prostate cancer to metastasize but not with the metastases themselves (10). These findings were the basis for the development and internal validation of a prognostic model that added plasma TGF-β1 and sIL-6R to standard clinical predictors (21). The addition of these biomarkers to the nomogram improved the prediction of biochemical recurrence by a statistically substantial margin over the results of the standard preoperative nomogram, but the predictive accuracy and calibration of the model were not perfect.

Nonetheless, our preliminary experience with the integration of serum biomarkers into a clinicopathologic model of prostate cancer outcome unveiled several important findings. First, a biomarker may reflect the disruption of a biochemical pathway by a particular mechanism. Second, given the complexity of the molecular abnormalities associated with prostate cancer, it is improbable that a single marker can accurately segregate tumors of similar clinicopathologic phenotypes into distinct prognostic categories. Therefore, combinations of independent yet complementary markers may provide a more accurate prediction of outcome compared with a single marker (27).

Given their importance in malignant dissemination, molecules that reflect extracellular matrix remodeling are particularly attractive targets as tumor biomarkers. The urokinase plasminogen activation system plays a key role in degrading the extracellular matrix and basement membrane, thereby promoting metastasis and angiogenesis. The inactive precursor of the serine protease, uPA, is activated by binding a specific membrane-bound or soluble cell surface receptor (uPAR), which accelerates the conversion of plasminogen into plasmin. Plasmin, in turn, degrades a wide spectrum of extracellular matrix proteins and basement membrane components through the activation of a cascade of proteases, including metalloproteinases. In prostate cancer, increased tissue levels of uPA and uPAR have been shown to be associated with tumor invasion, advanced cancer, and osteoblastic bone metastases (28, 29). Most importantly, in men without clinical evidence of metastases, preoperative plasma uPA was a strong predictor of biochemical progression after surgery (11).

Other promising candidate biomarkers are those associated with angiogenesis and cancer progression to metastasis such as VEGF, VCAM-1, and endoglin. VEGF promotes endothelial cell mitogenesis and survival, mediates chemotaxis of vascular endothelial cells, increases vascular permeability, inhibits maturation of antigen-presenting dendritic cells, and increases vasodilation (30). Malignant prostatic tissue produces significantly higher levels of VEGF than does benign prostatic tissue (31). Moreover, the blood levels of VEGF have been reported to be significantly increased in patients with metastatic prostate cancer (32).

VCAM-1 is a 90-kDa transmembrane glycoprotein that is transiently expressed on the surface of vascular endothelial cells in response to vascular endothelial growth factors and cytokines (33–35). In addition, VCAM-1 has been shown to function as an adhesion molecule facilitating primary prostate cancer metastasis (36). The plasma levels of VCAM-1 have been shown to be significantly elevated in patients with skeletal bone metastases, compared with patients with localized prostate cancer and controls. In preoperative and postoperative models that adjusted for the effects of standard predictors, both preoperative VEGF and VCAM-1 were associated with biochemical progression after radical prostatectomy.

Endoglin, or CD105, is a transmembrane glycoprotein that is typically expressed by human vascular endothelial cells (37, 38). Functionally, it is a cell surface coreceptor for TGF-β1 and TGF-β3 that modulates cellular responses to TGF-β in the early steps of endothelial cell proliferation (39). In prostate cancer, we have recently shown that endoglin is associated with metastases to pelvic lymph nodes and biochemical recurrence following radical prostatectomy (40).6

This study has several potential limitations. First and foremost are the limitations inherent to any retrospective study. Second, the sample size and relatively short follow-up may have limited our ability to detect small differences attributed to other variables. Despite this potential limitation, we showed an important and statistically significant gain in predictive accuracy. Moreover, the ability to predict the risk of early recurrence may be important for neoadjuvant and adjuvant treatment strategies because disease recurrence within 2 to 3 years of radical prostatectomy is associated with an increased risk of metastasis and cancer-specific mortality (41–43). At this stage, this predictive model is meant to assist, but not replace, clinical decision-making.

Our patient population was limited to patients undergoing radical prostatectomy. Therefore, the role of these biomarkers needs to be explored in other populations, such as patients treated with other treatment modalities (i.e., radiation therapy) and patients with a different range of disease severity (i.e., cT3a). Most importantly, not all patients who experience biochemical recurrence develop metastatic disease and die from prostate cancer (41, 42, 44–46). Biochemical recurrence may result from local failure related to residual disease present after radical prostatectomy, occult nodal or distant metastatic disease present at the time of surgery, or a combination of these (41, 42, 44–46). These forms of biochemical recurrence have variable progression rates with regard to metastases and eventual death. Therefore, it is necessary to assess the association of the promising biomarkers with prostate cancer metastasis and survival. These limitations represent hypothesis for future studies and should prompt future research in the area of biomarkers with particular interest on the most informative markers. These biomarkers need to be systematically and critically evaluated by multidisciplinary groups of experts before their introduction to patient care. Protocols delineating hierarchical scaling have been proposed for evaluating the weight of available evidence supporting the clinical value of any new biomarker under investigation (47). However, such a system has not yet been widely implemented by investigators assessing the qualitative strength of new prostate cancer biomarkers. The need is urgent to establish national multidisciplinary initiatives for coordinating the activities in the area of prostate cancer biomarkers, developing laboratory quality control programs for the analysis of cancer biomarkers, and producing guidelines for the appropriate clinical employment of each biomarker.


   Disclosure of Potential Conflicts of Interest
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References
 
No potential conflicts of interest were disclosed.


   Footnotes
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

6 R.S. Svatek, et al. Preoperative plasma endoglin levels predict biochemical progression after radical prostatectomy. Clin Cancer Res, in press. Back

Received 11/24/07; revised 1/27/08; accepted 2/ 9/08.


   References
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