The process of care for patients with prostate cancer is subject to different degrees of uncertainty. Patients and clinicians could, therefore, greatly benefit from improved prognostic instruments. One emerging tool is the cell cycle progression (CCP) score.
This systematic review assesses evidence on the value of the CCP instrument in prostate cancer treatment by reviewing current publications and integrating the results via a meta-analysis.
We performed a review of Medline and Embase in April 2014, according to Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA). Unpublished studies were retrieved from the 2013–2014 proceedings of major conferences in the field. Sixteen publications were selected for inclusion.
The results show that use of the CCP score is better than existing assessments at elucidating the aggressive potential of prostate cancer in an individual. The pooled hazard ratio for biochemical recurrence per 1-unit increase in the CCP score was 1.88 in a univariate model and 1.63 in a multivariate model. Four studies showed that CCP testing can impact the decisions of physicians regarding treatment, and potentially lead to a decrease in surgical interventions for low-risk patients.
This review offers a comprehensive overview of existing evidence on CCP testing, and provides clinicians, patients, and policy makers with a strong summary measure of its prognostic validity and clinical utility. It will be important to develop economic studies to measure the impact of such technology on health care systems.
In this paper, we review current evidence related to the cell cycle progression (CCP) score for patients with prostate cancer. We found good evidence suggesting that use of the CCP score improves prognosis, and can be a valuable tool for clinicians in treating patients. The economic benefits are yet to be studied.
Keywords: Prostate cancer, Cell cycle progression score, Prognostic value.
Prostate cancer is the most common form of cancer and the second leading cause of cancer death among men in several OECD countries, including the USA  and . Although the slow development of the disease facilitates early diagnosis  , clinical decision-making for prostate cancer patients is characterized by different degrees of uncertainty.
It has been shown that screening can result in severe overestimation of the number of prostate cancer cases by 25–80%  . In fact, prostate cancer does not progress in some patients, even in the absence of treatment  , and the prevalence of latent cases is estimated to be approximately 30% in patients older than 50 yr  . Urologists often have to make clinical decisions with insufficient information  , and anxiety may lead to overaggressive clinical management, even when the low-risk nature of the diagnosed disease would allow for active surveillance rather than surgical intervention  . It has been shown that radical treatment confers a survival advantage only in men with aggressive cancer. Low-risk patients fare no better than in the case of mere observation  . In addition, radical prostatectomy is associated with potential long-term complications , , , and  and a dramatic deterioration in quality of life  and . Therefore, accurate risk stratification is a key factor for appropriate and effective clinical management, as well as an instrument for avoiding unnecessary invasive treatment. More accurate prognosis could also facilitate rational allocation of health care resources.
Patients and clinicians could therefore greatly benefit from more advanced prognostic instruments to improve existing prediction models. One emerging and promising tool in this context is the cell cycle progression (CCP) score (Prolaris, Myriad Genetics Inc., Salt Lake City, UT, USA), an RNA expression signature already used to aid clinical decision-making in breast cancer , , , , and . The CCP methodology for this specific test in prostate cancer is based on analysis of 31 genes that determine cancer aggressiveness and predict the probability of disease progression  . Studies on the CCP score have investigated its prognostic value for both newly diagnosed patients and patients who have undergone prostatectomy but are at risk of disease recurrence.
Considering the novelty of this instrument and the great potential for health care systems, patients, and clinicians, the aim of our study was to assess the level of evidence for the prognostic value of the CCP instrument in prostate cancer via a systematic review of the literature and a meta-analysis of study results.
2. Evidence acquisition
2.1. Literature search
For our systematic review we adopted the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA)  . On March 17, 2014, a review protocol comprising three sections was established: background, review objectives, and methodology. The protocol, which is available on request, was followed rigorously for each phase of the review.
A systematic search of the literature was conducted from April 4, 2014 to identify studies on the prognostic validity, utility, or economic value of CCP testing in patients with prostate cancer. The Medline and Embase databases were searched for published studies, with no time or language filters. The results were integrated with unpublished studies retrieved through the 2013–2014 conference proceedings of the American Society of Clinical Oncology (ASCO), the American Urological Association (AUA), the European Association of Urology (EAU), and the Society of Urologic Oncology (SUO).
The search strategy included free text as well as controlled vocabulary built around the main construct of the research question according to the PICOS (patient characteristics, intervention, comparators, outcomes, study characteristics) framework and with reference to the 2005 optimal search strategy for prognostic tests of Wilczynski and Haynes  . Search terms included “cell cycle progression”, “cell cycle proliferation”, and “prostate”, as well as acronyms for these words. The study types considered included, among others, randomized control trials, observational studies, and cost analyses. Wildcards and truncation were employed to refine the strategy and adapt it to the different databases. Bibliographies of studies were checked for additional relevant articles.
2.2. Study selection and data extraction
Following removal of duplicates, two reviewers screened all titles, abstracts, and full texts independently according to the criteria in Table 1 . Agreement on eligibility was resolved by consensus in all cases. Interassessor reliability was assessed using a kappa statistic (considered slight for values from 0 to <0.2, fair from 0.2 to 0.4, moderate from 0.4 to <0.6, substantial from 0.6 to <0.8, and almost perfect from 0.8 to 1). Data were extracted using a customized template based on the PICOS statement (Supplementary Table 1 ).
|Inclusion criteria||Exclusion criteria|
|Studies evaluating the prognostic value of a cell cycle progression (CCP) gene panel in order to determine the aggressiveness of prostate cancer||All studies in languages other than English for which a translator is not available|
|Studies examining populations with prostate cancer and adjusting for comorbidities, if any||Animal or in vitro studies|
|Studies reporting economic data for CCP testing||Studies only including markers for prostate cancer other than CCP score by Myriad Genetics|
|Studies not including any of the following outcomes: cancer-specific mortality; biochemical recurrence/progression-free survival; metastasis-free survival; overall mortality adjusted for comorbidities; hazard ratio (HR) in univariate and multivariate analysis risk stratification decisions; change in patient care; economic data|
|Case reports; letters; comments; editorials; review papers|
2.3. Quality assessment
Published reviews of prognostic tests show substantial heterogeneity in study design, inclusion criteria, and other key characteristics , , and . The quality of selected studies for which the full text was available was assessed using and adapting the indications developed by the US Department of Health and Human Services specifically for systematic reviews of prognostic tests  . The checklist (Supplementary Table 2) was composed of 10 items, each of which was assessed using an ordinal scale with possible values of 0, 1, and 2, where 0 indicates “not clear” or “not a relevant item” and higher values indicate better quality. Scores for each item were compared and a consensus value was reached. This allowed for correct interpretation of the articles. The final score evaluated four main dimensions of the methodology: study design (items 1–4), methodology for CCP score measurement (items 5–6), outcome measurement (items 7–8), and study limitations and funding (items 9–10). Any economic study identified in the search was evaluated using the Drummond checklist for authors and peer reviewers of economic submissions to the British Medical Journal  . Two reviewers, including one urologist, reviewed the studies and scored them. The final score was expressed as a percentage. In the absence of a broadly accepted threshold for quality appraisal, categories were arbitrarily set: excellent quality, studies scoring >75%; good quality, studies scoring between 50% and 75%; and poor quality, studies scoring <50%. This procedure was applied to each quality assessment tool. Studies included in the systematic review were denoted as eligible, while those also included in the meta-analysis were denoted as evaluable.
2.4. Data analysis
A meta-analysis was conducted using the Comprehensive Meta-Analysis program (Biostat Inc., Engelwood, NJ, USA), which is a specialized software for conducting meta-analyses. Heterogeneity was estimated using Q, the weighted sum of squares on a standardized scale, and I2, which describes the percentage of variation across studies due to heterogeneity rather than chance. For Q, heterogeneity was considered to be significant for p values <0.10. I2 values ranged between 0% and 100%, where 0% indicates absence of heterogeneity and increasing values represent greater heterogeneity. I2 was considered to show moderate heterogeneity above 50%. We calculated pooled hazard ratios (HRs) with corresponding 95% confidence intervals (CIs). Values of p < 0.05 were considered statistically significant. We ran a meta-analysis using a random-effects model, which represents the general approach from which special cases such as the fixed-effects model are derived and generally leads to more conservative outcomes. Where no significant heterogeneity was detected between the studies being pooled, results for the fixed-effects model are also reported.
3. Evidence synthesis
3.1. Literature search
The study selection process is shown in Figure 1 . The primary literature search retrieved 171 articles. After duplicates were removed (16 cases), the remaining studies (155 articles) were screened by two reviewers independently: 50 were removed after title screening, 75 after reading the abstracts, and 14 after full text review (for published studies), leaving a final total of 16 relevant studies. Of these 16 studies, seven had already been published as of May 2014, while the remaining were conference abstracts. The kappa statistic was fair for title screening (0.26), moderate for the abstract screening phase (0.59), and almost perfect for full text screening (0.87).
3.2. Quality assessment
As shown in Table 2 , four studies were considered of excellent methodological quality , , , and . The remaining articles , , and  were ranked between 50% and 75% of the total possible score, with good overall quality. No study was scored as poor for quality.
|Study||Type of study||Endpoint||Definition of endpoint||Sample type||Treatment||Quality score (%)||Rating|
|Shore et al ||Clinical utility. Observational cohort, retrospective survey, based on prospective clinical study||Change in treatment after CCP test results||Physicians were asked if they would have changed their treatment decision for that patient had they known their CCP scores at the time||15 US urologists (294 evaluable patients)||Diagnostic biopsy||80||Excellent|
|Freedland et al ||Clinical validity. Observational cohort, retrospective||BCR(/DSM)||Phoenix criteria for BCR measurement (PSA >0.2 ng/ml). DSM, death with metastasis showing progression after androgen deprivation therapy||141||Diagnostic biopsy||75||Good|
|Bishoff et al ||Clinical validity. Observational cohort, retrospective||BCR||PSA >0.2 ng/ml||582||Simulated biopsy randomly removing a tissue cylinder (cohort I), diagnostic biopsy (cohort II, cohort III)||70||Good|
|Cuzick et al ||Clinical validity. Observational cohort, retrospective||DSM||World Health Organization criteria to distinguish prostate cancer death from other causes||349||Diagnostic biopsy||85||Excellent|
|Cuzick et al ||Clinical validity. Observational cohort, retrospective||BCR/DSM||Slight variation of the Phoenix criteria (>0.3 ng/ml) for BCR. DSM, death with disease progression after BCR||690||Radical prostatectomy specimens (US cohort); transurethral resection of the prostate (UK cohort)||80||Excellent|
|Kar et al ||Clinical utility. Observational cohort, prospective||Change in treatment after CCP test results||Change between intended treatment pre- and post-CCP test reporting||–||–||60||Good|
|Cooperberg et al ||Clinical validity. Observational cohort, prospective specimen collection, retrospective blinded evaluation design||BCR||PSA >0.2 ng/ml||413||Radical prostatectomy specimens||85||Excellent|
BCR = biochemical recurrence; CCP = cell cycle progression; DSM = disease-specific mortality; PSA = prostate-specific antigen.
3.3. Characteristics of the studies included
Following the ACCE framework categorization (Analytical validity, Clinical validity, Clinical utility, and Ethical, legal and social implications)  , we categorized four articles , , , and  as clinical utility studies, and 12 , , , , , , , , , , , and  as clinical validation studies. All articles referred to clinical observational cohort studies. No cost-effectiveness, cost-benefit, cost, or cost description analyses were found in the studies. One study had a prospective design  , while the others were retrospective. Three studies were conducted in a single center, while nine used data from multiple centers. For four of the unpublished studies , , , and , such information was not retrieved. The study with the largest sample involved 1606 patients  , while the smallest recruited 141 patients  .
Four studies , , , and  considered biochemical recurrence (BCR) as the only endpoint. Two articles considered disease-specific mortality (DSM) as the primary endpoint  and . Five studies included two different endpoints in the analysis: four , , , and  considered both BCR and DSM, while one  considered BCR and metastases. The remaining five eligible studies reported different types of outcomes. One study  looked at the presence of CCP values in benign prostate tissues. Four studies , , , and  investigated the relationship between CCP testing and clinical decision-making.
The CCP score was determined , , , , and  as described by Cuzick et al  using expression data for 31 CCP genes normalized by the expression of 15 housekeeping genes. Details were not available for the remaining studies (abstract only) , , , , , and . The samples used to calculate CCP scores varied across cohorts; in some cases, it was derived from biopsies , , , , , , , , and , transurethral resection of the prostate  and , or radical prostatectomy , , , , , and .
The studies looked at changes in the HR per 1-unit increase in the CCP score, that is, how each unit increase in the score translated into an increased risk of BCR or death from prostate cancer. Hazard ratios for every unit increase in CCP score are summarized in Table 3 for the univariate model and in Table 4 for the multivariate models, together with p values for prostate-specific antigen (PSA) and Gleason score (two scores commonly used to stage prostate cancer). The multivariate models included clinical and pathologic variables, such as extent of disease, age, clinical stage, and use of hormones.
|Univariate model: evaluable studies|
|Study||Endpoint||Patients (n)||Events (n)||CCP HR||CI low||CI high||CCP (p value)||PSA (p value)||Gleason (p value)|
|Freedland et al ||BCR||141||19||2.55||1.43||4.55||0.0017||<10–3||0.051|
|Bishoff et al  ; cohort I||BCR||283||48||2.1||1.5||2.8||<10–4||<10–4||<10–3|
|Bishoff et al  ; cohort II||BCR||176||83||1.3||1||1.7||0.027||0.012||0.34|
|Bishoff et al  ; cohort III||BCR||123||35||1.9||1.2||2.8||0.0028||<10–5||<10–3|
|Cooperberg et al ||BCR||413||82||2.1||1.6||2.9||<10–5||0.0035||<10–5|
|Cuzick et al  ; cohort I||BCR||353||132||2||1.6||2.4||<10–8||<10–17||<10–9|
|Cuzick et al  ; cohort II||DSM||337||76||2.9||2.4||3.6||<10–21||<10–13||<10–18|
|Cuzick et al ||DSM||349||90||2||1.6||2.5||<10–9||<10–4||<10–7|
|Univariate model: eligible studies|
|Study||Endpoint||Patients (n)||Events (n)||CCP HR||CI low||CI high||CCP (p value)|
|Salama et al ||BCR||141||16||2.71||1.44||5.09||0.002|
|Cooperberg et al ||BCR||230||–||1.82||1.32||2.52||<0.001|
|Cuzick et al ||DSM||757||136||2.32||2||2.7||<10–17|
|Freedland et al ||DSM||141||19||3.77||1.37||10.4||0.013|
BCR = biochemical recurrence; CI = confidence interval; DSM = disease-specific mortality.
|Multivariate model: evaluable studies|
|Study [Refs]||Endpoint||Patients (n)||Events (n)||CCP HR||CI low||CI high||CCP (p value)||PSA (p value)||Gleason score (p value)|
|Freedland et al ||BCR||141||19||2.1||1||4.2||0.035||0.087||0.2|
|Bishoff et al  ; cohort I||BCR||283||48||1.7||1.2||2.3||0.0033||0.0029||0.038|
|Bishoff et al  ; cohort II||BCR||176||83||1.3||1||1.7||0.031||0.029||0.65|
|Bishoff et al  ; cohort III||BCR||123||35||1.6||1||2.4||0.041||<10–3||0.14|
|Cooperberg et al ||BCR||413||82||2||1.4||2.8||<10–4||0.12||0.17|
|Cuzick et al  ; cohort I||BCR||353||132||1.7||1.4||2.2||<10–5||<10–8||0.015|
|Cuzick et al  ; cohort II||DSM||337||76||2.6||1.9||3.4||<10–10||<10–7||0.028|
|Cuzick et al ||DSM||349||90||1.7||1.3||2.1||<10–4||0.017||0.0022|
|Multivariate model: eligible studies|
|Study [Refs]||Endpoint||Patients (n)||Events (n)||CCP HR||CI low||CI high||CCP (p value)|
|Salama et al ||BCR||141||16||2.53||1.14||5.61||0.019|
|Cooperberg et al ||BCR||230||–||1.84||1.1||3.05||0.021|
|Cuzick et al ||DSM||585||–||1.86||1.52||2.27||<10–6|
BCR = biochemical recurrence; CI = confidence interval; DSM = disease-specific mortality.
Schlomm et al  reported HRs for BCR for a 1-unit increase in CCP of 2.03 (95% CI 1.48–2.79) and 1.6 (95% CI 1.15–2.24) when adjusting for Gleason score and PSA, respectively (p = 3.1 × 10–5 and 0.0068). They also found that the CCP score was an independent predictor of pathologic stage in a multivariate model. Brawer et al  found that combining CCP score with CAPRA (a pretreatment score based on patient age, PSA, biopsy Gleason score, clinical T stage, and percentage of positive biopsy cores) improves prognostic information; in particular the combined score (HR 2.27, 95% CI 1.63–3.16, p = 1.2 × 10−7) predicted disease-specific mortality better than CAPRA alone. Another abstract by Brawer et al  found that CCP was a highly significant predictor of BCR and of cancer-specific death.
One study by Carvalho et al  , presented at the AUA conference in 2013, investigated CCP values for benign prostate tissues and concluded that the significant correlation between CCP for prostate tumor tissues and for cancer-free tissues allows CCP scores to be generated even from needle biopsies containing few cancer cells.
The remaining four studies investigated the relationship between CCP testing and clinical decision-making. Two studies  and  indicated that after CCP testing, more than 50% of men were reassigned to a different risk group. Shore et al  reported that 32% of CCP score results would lead to a likely or certain change in treatment. Kar et al  reported a 49.5% reduction in surgical interventions and a 29.6% decrease in radiation therapy among patients who had previously been recommended such interventions. Shore et al  used CCP scores to reclassify cancer aggressiveness more accurately in approximately 300 patients.
Meta-analyses were conducted on five evaluable studies ( Fig. 2 ). Separate meta-analyses were performed for the two endpoints (BCR , , , and  and DSM  and ) and models (univariate and multivariate). A meta-analysis of HR values from univariate models for both endpoints and multivariate models for studies reporting DSM as an endpoint  and  found moderate heterogeneity, taking into account a random-effect analysis. The results for studies using DSM  and  as an endpoint showed a pooled HR of 2.42 in the univariate model (p = 0.01, I2 = 83.85) and 2.08 in the multivariate model (p = 0.04, I2 = 75.43), meaning that for a 1-unit increase in CCP, the risk of death from prostate cancer more than doubled.
A meta-analysis of HR values from univariate models for studies reporting BCR as an endpoint , , , and  found moderate heterogeneity, taking into account a random-effects model (p = 0.07 for Q, I2 = 49.91). The pooled HR for a 1-unit increase in CCP was 1.88 in the univariate model. However, no evidence of heterogeneity was found when pooling values from multivariate models for studies reporting BCR as an endpoint (p = 0.44, I2 = 0.00). In this case, we ran both random-effects and fixed-effects analyses, which led to the same pooled HR value (1.634), meaning that for each additional unit in the CCP score, the risk of BCR almost doubled.
The forest plots in Figure 2 show the HRs for each study (represented by the squares), which are proportional to the sample size in each individual study. The pooled effect estimates are represented by the diamonds below each group of studies. The horizontal lines for 95% CIs indicate that outcomes are clinically meaningful in almost all cases. In fact, they are far from the point of no difference, which is represented by the vertical line equal to 1 (lower limit is equal to 1 only for cohort II in study  ).
The diagnosis and treatment of prostate cancer are associated with considerable clinical and economic burdens. Prostate cancer represents the fourth most expensive type of cancer in terms of economic burden in European countries (€8.4 billion annually, 7% of the total economic cost of cancer care)  and in the USA (US$9.9 billion in 2006)  .
The current systematic review was designed to assess and summarize the level of evidence on the prognostic value of CCP score testing in prostate cancer and its impact on clinical practice and disease management. We also looked for economic evidence, which we found has yet to be investigated. Besides evidence from published studies, the analysis identified a growing number of forthcoming studies on the clinical validity and utility of this test. The results show that the CCP score describes the aggressive potential of an individual's prostate cancer better than existing assessments alone. This is particularly relevant considering that current prediction models for the prognosis of prostate cancer need improved tools providing more accurate information on the heterogeneous nature of prostate cancer  . In fact, an exact prognostic prediction of the clinical course of specific diseases allows evidence-based and individualized treatment decisions, and is particularly important in improving treatment appropriateness and quality of care for patients  .
The clinical validity of the CCP score has been tested in multiple patient cohorts and diverse clinical settings, and for multiple clinical outcomes, in particular BCR and DSM. Subsequent studies will need to ascertain the association between the CCP score and metastatic disease. Different sample types have been used to test the CCP score, such as tissue from needle biopsy and radical prostatectomy, and the evidence so far does not suggest the superiority of one sample type over another. The critical role of CCP testing in the prognosis of prostate cancer contributes to its clinical utility. Studies addressing the role of CCP in decision-making by practitioners show promising results , , , and . This is particularly important to avoid long-term complications due to unnecessary radical intervention , , , and  and the likely deterioration of quality of life that follows  and .
These findings potentially have important implications in terms of improved quality of life and resource savings for health care systems. In Italy, for instance, the incidence of prostate cancer is estimated at 42 000 new cases per annum  . Of those, it can reasonably be estimated that 40–45% are low-risk patients  and . If the findings of Kar et al  are applied to the Italian setting, then we could hypothesize that a percentage as high as 24.4% of low-risk patients (almost 4100 patients/yr) would change their treatment from intervention to nonintervention (no prostatectomy) after execution of a CCP test. Conversely, 7.4% of the same low-risk group (>1200 patients/yr) would switch from nonintervention to intervention (prostatectomy) after obtaining their CCP score. Provided that appropriate modeling techniques are applied, and that all costs and cost savings arising from introduction of the test in everyday clinical practice are taken into account, the overall result would probably involve not only improvements in treatment appropriateness and quality of life, but also potentially overall savings for the National Health Service.
This study has a number of limitations. Individual-level data used in the studies selected were not accessible. Use of patient data instead of the aggregate approach would have facilitated standardization of results  and enriched the overall analysis  . Moreover, the study did not address difficulties related to the determination of cutoff points for prognostic studies  .
This systematic review offers a comprehensive overview of the state of the art of existing evidence on CCP testing with the Prolaris test, and provides clinicians, patients, and policy-makers with a strong summary value for its prognostic validity. It will be important to develop economic studies to measure the impact on health care systems, patients, and families of more appropriate treatments (eg, reduction in surgical interventions or radiotherapy) that would inevitably lead to a decrease in unnecessary complications and side effects and would thus preserve or increase productivity and quality of life for patients. Health technology assessment is widespread in almost all industrialized countries and has become the main criterion for deciding whether it is worth investing in health innovations. In the very near future, clinical evidence of CCP testing may be deemed sufficiently robust by scientific communities to allow its introduction in clinical practice with the potential to improve the appropriateness of treatment. Therefore, it is important to develop economic studies of the impact of such technologies on health care systems, patients, and families.
Author contributions: Silvia Sommariva had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Lazzeri, Montorsi, Ricciardi, Sommariva, Tarricone.
Acquisition of data: Sommariva, Tarricone.
Analysis and interpretation of data: Lazzeri, Sommariva, Tarricone.
Drafting of the manuscript: Sommariva, Tarricone.
Critical revision of the manuscript for important intellectual content: Lazzeri, Montorsi, Ricciardi, Tarricone.
Statistical analysis: Sommariva.
Obtaining funding: Tarricone.
Administrative, technical, or material support: Sommariva.
Other (specify): None.
Financial disclosures: Silvia Sommariva certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None.
Funding/Support and role of the sponsor: The study was supported by Myriad Genetics. The sponsor played a role in reviewing the manuscript.
Acknowledgments: This study was supported by a partial unrestricted grant from Myriad Genetics. We thank Dr. Oriana Ciani for revising the first draft of the manuscript. We also thank the anonymous reviewers for their insightful comments.
Appendix A. Supplementary data
-  US Cancer Statistics Working Group. United States cancer statistics: 1999–2010 incidence and mortality web-based report. Atlanta, GA: Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2013.
-  Health at a glance 2011: OECD data. Mortality from cancer. http://www.oecd-ilibrary.org/sites/health_glance-2011-en/01/04/index.html?itemId=/content/chapter/health_glance-2011-7-en
-  Istituto Toscano Tumori. Carcinoma della prostata: raccomandazioni cliniche. http://www.ittumori.it/ITA/pubblicazioni/raccomandazioni_cliniche_2012/05_prostata.pdf
-  G.L. Lu-Yao, P.C. Albertsen, D.F. Moore, et al. Outcomes of localized prostate cancer following conservative management. JAMA. 2009;302:1202-1209 Crossref
-  C.R. Chen. Making individualized decisions in the midst of uncertainties: the case of prostate cancer and biochemical recurrence. Eur Urol. 2013;64:916-918
-  D.M. Latini, S.L. Hart, S.J. Knight, et al. The relationship between anxiety and time to treatment for patients with prostate cancer on surveillance. J Urol. 2007;178:826-831
-  T.J. Wilt, M.K. Brawer, M.K. Jones, et al. Radical prostatectomy versus observation for localized prostate cancer. N Engl J Med. 2012;367:203-213 Crossref
-  O. Dillioglugil, B.D. Leibman, N.S. Leibman, M.W. Kattan, A.L. Rosas, P.T. Scardino. Risk factors for complications and morbidity after radical retropubic prostatectomy. J Urol. 1997;157:1760-1767
-  H. Heinzer, M. Graefen, J. Noldus, P. Hammerer, H. Huland. Early complication of anatomical radical retropubic prostatectomy: lessons from a single-center experience. Urol Int. 1997;59:30-33 Crossref
-  P.J. Davidson, D. van den Ouden, F.H. Schroeder. Radical prostatectomy: prospective assessment of mortality and morbidity. Eur Urol. 1996;29:168-173
-  W.J. Catalona, G.F. Carvalhal, D.E. Mager, D.S. Smith. Potency, continence and complication rates in 1,870 consecutive radical retropubic prostatectomies. J Urol. 1999;162:433-438
-  G.J. Huang, N. Sadetsky, D.F. Penson. Health related quality of life for men treated for localized prostate cancer with long-term followup. J Urol. 2010;183:2206-2212 Crossref
-  P.D. Smith, M.T. King, S. Egger, et al. Quality of life three years after diagnosis of localised prostate cancer: population based cohort study. BMJ. 2009;339:b4817
-  M.J. van de Vijver, Y.D. He, L.J. van’t Veer, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999-2009 Crossref
-  Y. Wang, J.G. Klijn, Y. Zhang, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365:671-679
-  H.Y. Chang, D.S. Nuyten, J.B. Sneddon, et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A. 2005;102:3738-3743 Crossref
-  C. Sotiriou, C. Desmedt. Gene expression profiling in breast cancer. Ann Oncol. 2006;17(Suppl 10):259-262
-  M. Loddo, S.R. Kingsbury, M. Rashid, et al. Cell-cycle phase progression analysis identifies unique phenotypes of major prognostic and predictive significance in breast cancer. Br J Cancer. 2009;100:959-970 Crossref
-  A. Kern, A.W. Partin. Genetic tests for prostate cancer. Rev Urol. 2013;15:208-209
-  D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151:264-269 Crossref
-  N.L. Wilczynski, B. Haynes. Optimal search strategies for detecting clinically sound prognostic studies in EMBASE: an analytic survey. J Am Med Inform Assoc. 2005;12:481-485 Crossref
-  P.A. Hall, J.J. Going. Predicting the future: a critical appraisal of cancer prognosis studies. Histopathology. 1999;35:489-494 Crossref
-  D.G. Altman, R.D. Riley. Primer: an evidence-based approach to prognostic markers. Nat Clin Pract Oncol. 2005;2:466-472 Crossref
-  M.L. Sousa, A.L. Ribeiro. Systematic review and meta-analysis of diagnostic and prognostic studies. Arq Bras Cardiol. 2009;92:229-238
-  T.S. Rector, B.C. Taylor, T.J. Wilt. Systematic review of prognostic tests. Methods guide – Chapter 12. Department of Health and Human Services. Agency for Healthcare Research and Quality;. 2012;
-  M. Drummond, M. Schulpher, G. Torrance, et al. Methods for the economic evaluation of health care programmes. (Oxford University Press, Oxford, 2005)
-  M.R. Cooperberg, J.P. Simko, J.E. Cowan, et al. Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J Clin Oncol. 2013;31:1428-1434 Crossref
-  J. Cuzick, G.P. Swanson, J. Fisher, et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol. 2011;12:245-255 Crossref
-  N. Shore, R. Concepcion, D. Saltzstein, et al. Clinical utility of a biopsy-based cell cycle gene expression assay in localized prostate cancer. Curr Med Res Opin. 2013;30:547-553
-  J. Cuzick, D.M. Berney, G. Fisher, et al. Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br J Cancer. 2012;106:1095-1099 2012 Crossref
-  S.J. Freedland, L. Gerber, J. Reid, et al. Prognostic utility of cell cycle progression score in men with prostate cancer after primary external beam radiation therapy. Int J Radiat Oncol Biol Phys. 2013;86:848-853 Crossref
-  J.T. Bishoff, S.J. Freedland, L. Gerber, et al. Prognostic utility of the cell cycle progression (CCP) score generated from needle biopsy in men treated with prostatectomy. J Urol. 2014;192:409-414 Crossref
-  E.D. Crawford, M.C. Scholz, A.J. Kar, et al. Cell cycle progression score and treatment decisions in prostate cancer: results from an ongoing registry. J Clin Oncol. 2014;30:1025-1031 Crossref
-  Centers for Disease Control and Prevention. ACCE model process for evaluating genetic tests. January 3, 2010. http://www.cdc.gov/genomics/gtesting/ACCE/
-  D.E. Crawford, N. Shore, P.T. Scardino, et al. CCP score and risk stratification for prostate cancer patients at biopsy. J Clin Oncol. 2014;32(Suppl 4):47
-  N.D. Shore, B. Abbott, R.S. Concepcion, et al. Stratification of risk for patients with prostate cancer at biopsy using CCP score. J Clin Oncol. 2013;31(Suppl 6):127
-  T. Schlomm, Z. Sangale, J.S. Lanchbury, et al. Value of cell cycle progression (CCP) score to predict biochemical recurrence and definitive post-surgical pathology. J Clin Oncol. 2013;31(Suppl 15):5043
-  M.K. Brawer, J.M. Cuzick, M.R. Cooperberg, et al. Prolaris: a novel genetic test for prostate cancer prognosis. J Clin Oncol. 2013;31(Suppl 15):5005
-  J.M. Cuzick, S. Stone, G. Fisher, et al. Prognostic value of a 46-gene cell cycle progression (CCP) RNA signature for prostate cancer death in a conservatively managed watchful waiting needle biopsy cohort. J Urol. 2014;191(4 Suppl):e936 Crossref
-  M.R. Cooperberg, S.J. Freedland, T. Schlomm, et al. Predicting radical prostatectomy outcome: cell cycle progression (CCP) score compared with primary Gleason grade among men with clinical Gleason less than 7 who are upgraded to Gleason 7. J Clin Oncol. 2014;32(Suppl 4):13
-  F. Carvalho, W. Welbourn, J. Reid, et al. Evidence for a cell cycle proliferation “field effect” in prostate cancer. J Urol. 2013;189(Suppl 4):e605 Crossref
-  M.K. Brawer, M.L. Cooperberg, S. Freedland, et al. Development and validation of a mutivariate model combining cell cycle progression score with CAPRA to predict prostate cancer mortality in a conservatively managed cohort. J Clin Oncol. 2013;31(Suppl 6):67
-  J.K. Salama, S. Freedland, L. Gerber, et al. Cell cycle progression (CCP) score significantly predicts PSA failure after EBRT. Int J Radiat Oncol Biol Phys. 2013;87:125
-  R. Luengo-Fernandez, J. Leal, A. Gray, R. Sullivan. Economic burden of cancer across the European Union: a population based cost analysis. Lancet Oncol. 2013;14:1165-1174 Crossref
-  C.G. Roehrborn, L.K. Black. The economic burden of prostate cancer. BJU Intl. 2011;108:806-813
-  H. Miyake, M. Fujisawa. Prognostic prediction following radical prostatectomy for prostate cancer using conventional as well as molecular biological approaches. Int J Urol. 2013;20:301-311 Crossref
-  U. Capitanio, A. Briganti, A. Gallina, et al. Predictive models before and after radical prostatectomy. Prostate. 2010;70:1371-1378
-  Associazione Italiana di Oncologia Medica. Linee guida carcinoma della prostata AIOM 2013. http://www.aiom.it
-  R. Riley, P.C. Lambert, G. Abo-Zaid. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010;340:c221 Crossref
a Centre for Research on Health and Social Care Management, Bocconi University, Milan, Italy
b Department of Policy Analysis and Public Management, Bocconi University, Milan, Italy
c Division of Oncology, Unit of Urology, Ospedale San Raffaele, Università Vita-Salute San Raffaele, Milan, Italy
d Department of Public Health, Catholic University of the Sacred Heart, Rome, Italy
Corresponding author. Università Bocconi, via Roentgen, 1, 20136 Milan, Italy. Tel. +39 02 58362766; +39 33 8792 4753.
© 2014 European Association of Urology, Published by Elsevier B.V.