Localised prostate cancer in elderly men aged 80-89 years, findings from a population-based registry.

OBJECTIVES: To investigate the rate of prostate cancer-specific mortality (PCSM) and disease characteristics in patients diagnosed with localised prostate cancer at age 80-89 years in comparison with men diagnosed at age 70-79 years. PATIENTS AND METHODS: This is a retrospective study of data from the South Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC). Included were men diagnosed between 2005 and 2014, aged ≥70 years with no evidence of metastatic disease at presentation. Propensity score matching and competing risk Fine and Grey regression were used to assess the chance of treatment (curative vs non-curative) and treatment effect on PCSM. RESULTS: Of the 1 951 eligible patients, 1 428 (76%) were aged 70-79 years and 460 (24%) were aged 80-89 years at diagnosis, with a median (interquartile range) age of 74 (72-76) and 83 (81-85) years, respectively. The 80-89 years group had higher Gleason scores and Prostate Specific Antigen (PSA) values (all P < 0.001) in comparison with the younger group. The 80-89 years group were less likely to be treated with curative treatment (odds ratio 0.12, 95% confidence interval 0.09-0.16; P < 0.001). The proportion of deaths attributable to prostate cancer was similar in both groups: 73 of 263 deaths (28%) in the 80-89 years group vs 97 of 310 deaths (31%) in the 70-79 years group. The risk of PCSM in individuals treated with curative intent was reduced in both groups. CONCLUSIONS: The proportion of prostate cancer deaths was similar in both groups. These findings support carefully selected individualised management of elderly patients diagnosed with localised prostate cancer.

Comparative analysis of three risk assessment tools in Australian patients with prostate cancer.

UNLABELLED: What's known on the subject? and What does the study add? Prognostic tools, such as the Cancer of the Prostate Risk Assessment (CAPRA) score and the 1998 Kattan and 2006 Stephenson nomograms, predicting biochemical recurrence after radical prostatectomy are widely used for treatment decision making and counselling patients. However, tools derived in certain cohorts tend to perform less well when they are applied to populations that are dissimilar in terms of population or disease characteristics, health systems or treatment practices. Some of the loss in accuracy of a prognostic tool is a consequence of unknown factors and hence the performance of a tool when applied to a different population is unknown and largely unpredictable. This study validates these widely used tools in South Australian patients treated at three public hospitals. All three tools discriminated well according to risk of recurrence in these patients. However, when compared against observed rates of recurrence, it was found that predictions of recurrence varied widely between the three tools, suggesting that their use in counselling patients on such risk may not be appropriate. Interestingly, the oldest of the three tools (Kattan 1998) was the best predictor of absolute risk of recurrence. In the paper, this is linked to later adoption of updated Gleason grading, among other factors. SUMMARY: In many countries, prognostic tools, which draw on the experience of thousands of patients with cancer, are used to predict cancer outcomes, but accuracy varies. This paper compares the accuracy of three widely used tools predicting prostate cancer recurrence after surgery in Australian patients. The results show that all tools were good at predicting which patients were most likely to experience recurrence and which were least. However, prediction of absolute risk varied and the oldest tool was the most accurate. OBJECTIVE: • To compare performance of the CAPRA score and two commonly used risk assessment nomograms, the 1998 Kattan and the 2006 Stephenson, in an untested Australian cohort. PATIENTS AND METHODS: • We present data on 635 men from the South Australian Prostate Cancer Clinical Outcomes Database who underwent radical prostatectomy between January 1996 and May 2009 and had all required variables for predicting biochemical recurrence (BCR). • BCR was defined as prostate-specific antigen ≥ 0.2 ng/mL or secondary treatment for a rising prostate-specific antigen. • Accuracy was evaluated using Harrell's concordance index, plotting calibration curves, and constructing decision analysis curves. RESULTS: • Concordance indices were high for all three tools: 0.791, 0.787 and 0.744 for the 2006 Stephenson nomogram, CAPRA score and 1998 Kattan nomogram respectively. • At 3 years, calibration of the tools (agreement between predicted and observed BCR-free probability) was close to ideal for the 1998 Kattan nomogram, whereas the 2006 Stephenson model underestimated and the CAPRA model overestimated BCR-free probability. • The 1998 Kattan and 2005 CAPRA tools performed better than the 2006 Stephenson nomogram across a wide range of threshold probabilities using decision curve analysis. CONCLUSION: • All three tools discriminate between patients' risk effectively. • Absolute estimates of risk are likely to vary widely between tools, however, suggesting that models should be validated and, if necessary, recalibrated in the population to which they will be applied. • Recent development does not mean a nomogram is more accurate for use in a particular population.

PSA response to antiandrogen withdrawal: a systematic review and meta-analysis.

BACKGROUND: Antiandrogen withdrawal (AAW) response is the paradoxical decrease in prostate-specific antigen (PSA) following the withdrawal of antiandrogen in patients with advanced prostate cancer. Currently, the reported literature on the proportion of patients exhibiting AAW response and the differences in PSA response between the types of antiandrogens is unclear. METHODS: This review aimed to explore the PSA response to AAW and to identify if the response depends on the type of antiandrogens. A literature search was performed using databases PubMed, Cochrane and EMBASE with a cut-off date of 23rd of November 2020. Studies reporting on outcomes of AAW and prostate cancer were included. Studies were screened by two reviewers and relevant data extracted. Meta-analysis of outcomes was reported using random-effects and fixed-effects model. A subgroup analysis was performed for type of antiandrogen. RESULTS: From 450 studies, 23 were included with a total of 1474 patients with advanced prostate cancer were available for further analysis. Overall, 395 (26%) patients had any reduction in PSA levels (95% CI: 20-32%) and 183 (11%) patients had a ≥50% reduction in PSA levels (95% CI: 6-16%). Among the 1212 patients on first-generation antiandrogens, 30% (95% CI: 23-38%) had any PSA decline with 15% patients having a ≥50% PSA decline (95% CI: 8-22%). In contrast, among the 108 patients on second-generation antiandrogens, 7% (95% CI: 0-13%) had any PSA decline and only 1% (95% CI: 0-5%) had a ≥50% PSA decline. Also, among the 154 patients on androgen synthesis inhibitors, 26% (95% CI: 19-33%) had any PSA decline and only 4% (95% CI: 0-13%) had a ≥50% PSA decline. CONCLUSIONS: One-fourth of patients treated with AAW show a PSA response. However, PSA response to AAW is uncommon with second-generation antiandrogens and androgen synthesis inhibitors. Further research is required to understand the differences in response between the types of antiandrogen.

Abnormal PSA tests--delays in referral.

BACKGROUND: The main benefit of prostate specific antigen (PSA) testing is to help detect prostate cancer at an early, curable stage. Delays between the first abnormal PSA test and biopsy can undermine that benefit, but have not yet been studied. We investigated delays before biopsy together with associated PSA increases as an indicator of disease progression. METHODS: We identified 241 patients with a primary care referral because of an elevated PSA result (>4 ng/mL) and no previous prostate biopsy. Prostate specific antigen results and intervals between PSA testing, specialist clinic referral, appointment and biopsy were stratified by age. RESULTS: Median times between first abnormal PSA, referral, consultation and biopsy were modest but associated with increases in PSA. Extended delays (>20 months) between first abnormal PSA and referral occurred in 25% of younger men. A PSA result less than 10 ng/mL was the best predictor of a delay to refer. DISCUSSION: Rising PSA and possible cancer progression during investigation for prostate cancer suggest that prompt care is advisable.

Prostate cancer outcomes and delays in care.

OBJECTIVES: To examine the survival effect of treatment delays from the time of confirmed diagnosis of prostate cancer to first treatment in an Australian population. METHODS: Three thousand one hundred and forty patients were identified from the South Australian Prostate Cancer Clinical Outcomes Collaborative database for analysis. Selected patients had dates recorded for both diagnosis and treatment. We examined the effect of treatment delay (the time from diagnosis to date of first treatment) on survival using Cox and competing risks regression and compared quartiles of delay across the cohort. Adjustment was made for age, PSA levels, treatment modality and Gleason score. Outcomes included overall survival (OS) and prostate cancer-specific mortality (PCSM). RESULTS: Quartiles of delay were as follows (days)-Q1: 35, Q2: 86, Q3: 138.0, Q4: 264. Shorter delays were associated with hormonal treatment, high Gleason score and high PSA values. Measuring PCSM with Q2 as reference, age-adjusted associations were-Q1: sHR 4.37 (2.75-6.94), Q3: sHR 1.29 (0.73-2.28), Q4: sHR 1.55 (0.91-2.63). After additional adjustment for treatment type, Gleason score and PSA, Q1 remained at increased risk [sHR 2.46 (1.10-5.54)]. A similar trend was observed for OS. In analysis stratified by Gleason score, delays were not significantly associated with OS. CONCLUSIONS: Factors associated with shorter delay in treatment include high Gleason score, high PSA and hormonal treatment. After adjustment for these variables, increased delays were not associated with OS or PCSM in this cohort. The nonlinear association of delay with risk may explain conflicting reports in the literature.

Stromal androgen receptor regulates the composition of the microenvironment to influence prostate cancer outcome.

Androgen receptor (AR) signaling in stromal cells is important in prostate cancer, yet the mechanisms underpinning stromal AR contribution to disease development and progression remain unclear. Using patient-matched benign and malignant prostate samples, we show a significant association between low AR levels in cancer associated stroma and increased prostate cancer-related death at one, three and five years post-diganosis, and in tissue recombination models with primary prostate cancer cells that low stromal AR decreases castration-induced apoptosis. AR-regulation was found to be different in primary human fibroblasts isolated from adjacent to cancerous and non-cancerous prostate epithelia, and to represent altered activation of myofibroblast pathways involved in cell cycle, adhesion, migration, and the extracellular matrix (ECM). Without AR signaling, the fibroblast-derived ECM loses the capacity to promote attachment of both myofibroblasts and cancer cells, is less able to prevent cell-matrix disruption, and is less likely to impede cancer cell invasion. AR signaling in prostate cancer stroma appears therefore to alter patient outcome by maintaining an ECM microenvironment inhibitory to cancer cell invasion. This paper provides comprehensive insight into AR signaling in the non-epithelial prostate microenvironment, and a resource from which the prognostic and therapeutic implications of stromal AR levels can be further explored.

Prostate-specific antigen (PSA) rate of decline post external beam radiotherapy predicts prostate cancer death.

BACKGROUND AND PURPOSE: To assess the association between PSA velocity (PSAV) in the first 24 months after external beam radiotherapy (EBRT) and prostate cancer-specific mortality (PCSM) and all cause mortality. MATERIALS AND METHODS: All eligible patients in the South Australian (SA) Prostate Cancer Clinical Outcomes registry were followed. 848 Patients treated by definitive EBRT with more than one PSA recorded in the two year post-treatment were included. We calculated PSAV by linear regression. RESULTS: The mean number of PSA measurements in the 2year period was 4.4 (SD1.9). The median PSAVs across quartiles (Q1-Q4) were -4.17, -1.29, -0.38 and 0.20ng/ml/yr. In multivariable analysis, a U-shaped relationship was seen between PSAV and PCSM with Q1-Q4 hazard ratios (HR) being 3.82 (1.46-10.00), 3.07 (1.10-8.58), 1, 5.15 (1.99-13.30) respectively. HR for all cause mortality in a similar model were 1.79 (1.07-2.98), 1.55 (0.93-2.59), 1.00 and 1.74 (1.04-2.90) for Q1 to Q4 respectively. A rapid PSA decline in the first year was a strong predictor of PCSM. However, in the second year PSA increase was positively associated with PCSM. CONCLUSION: A rapid decline in PSA in the first year following EBRT is positively associated with PCSM. This may be a useful early indicator of the need for additional therapies.