Clinical Management and Burden of Prostate Cancer: A Markov Monte Carlo Model

<div><p>Background</p><p>Prostate cancer (PCa) is the most common non-skin cancer among men in developed countries. Several novel treatments have been adopted by healthcare systems to manage PCa. Most of the observational studies and randomized trials on PCa have concurrently evaluated fewer treatments over short follow-up. Further, preceding decision analytic models on PCa management have not evaluated various contemporary management options. Therefore, a contemporary decision analytic model was necessary to address limitations to the literature by synthesizing the evidence on novel treatments thereby forecasting short and long-term clinical outcomes.</p><p>Objectives</p><p>To develop and validate a Markov Monte Carlo model for the contemporary clinical management of PCa, and to assess the clinical burden of the disease from diagnosis to end-of-life.</p><p>Methods</p><p>A Markov Monte Carlo model was developed to simulate the management of PCa in men 65 years and older from diagnosis to end-of-life. Health states modeled were: risk at diagnosis, active surveillance, active treatment, PCa recurrence, PCa recurrence free, metastatic castrate resistant prostate cancer, overall and PCa death. Treatment trajectories were based on state transition probabilities derived from the literature. Validation and sensitivity analyses assessed the accuracy and robustness of model predicted outcomes.</p><p>Results</p><p>Validation indicated model predicted rates were comparable to observed rates in the published literature. The simulated distribution of clinical outcomes for the base case was consistent with sensitivity analyses. Predicted rate of clinical outcomes and mortality varied across risk groups. Life expectancy and health adjusted life expectancy predicted for the simulated cohort was 20.9 years (95%CI 20.5–21.3) and 18.2 years (95% CI 17.9–18.5), respectively.</p><p>Conclusion</p><p>Study findings indicated contemporary management strategies improved survival and quality of life in patients with PCa. This model could be used to compare long-term outcomes and life expectancy conferred of PCa management paradigms.</p></div

Predicted outcomes by risk groups.

<p>PCa – prostate cancer, mCRPC – metastatic castrate resistant prostate cancer, 95%CI –95% confidence interval<b>.</b></p><p>Predicted outcomes by risk groups.</p

Schematic diagram of the computer simulation model.

<p>For each simulation, patients transited from left to right of the model. Incident PCa cases were distributed to active surveillance or curative intent initial treatment ascertained by level of risk at diagnosis. Straight arrows indicated potential transition pathways over successive cycles. Curved arrows indicated cases remained on that health state over successive cycles. Transition between health states was ascertained by state transition probabilities and disease evolution. Following active surveillance or initial treatment, patients were subsequently treated for PCa recurrence and metastatic castration resistant prostate cancer (i.e. mCRPC) ascertained by state transition probabilities and disease evolution over successive cycles. Patients deceased from PCa or other causes exited the model.</p

Predicted outcomes by treatment strategies.

<p>AS-active surveillance, RP-radical prostatectomy, BT-brachytherapy, IMRT-intensity modulated radiation therapy, ADT-androgen deprivation therapy, mCRPC-metastatic castrate resistant prostate cancer, PCa-prostate cancer, 95%CI –95% confidence interval,+multimodal treatment.</p><p>Predicted outcomes by treatment strategies.</p

Response of a panel of bladder cancer cell lines to ionizing radiation.

<p>Plated cells were exposed to ionizing radiation to measure the effects on growth by clonogenic assay, as described in Methods. (<b>A</b>) Based on the gathered results, we were able to classify these cell lines as radiation–sensitive, moderately sensitive and -relatively resistant. (<b>B</b>) The RAD001 IC50 was plotted against the slope of the curve for each cell line in the clonogenic assay when treated with IR.</p

Immunohistochemical p21 levels in mouse xenograft paraffin sections.

<p>(<b>A</b>) Immunohistochemistry was used to detect the levels of p21 in paraffin-embedded mouse xenograft bladder cancer tissues treated with placebo, IR, RAD001 and in combination. (<b>B</b>) Quantification of the immunohistochemistry data revealed a significant increase in p21 expression as observed in tumors treated with ionizing radiation and in combination compared to the placebo and RAD001 treatment.</p

Annual rates and probabilities for base case and sensitivity analyses.

<p>PCa – prostate cancer,+multimodal treatment, Not applicable – treatment option not considered for base case.</p><p>Annual rates and probabilities for base case and sensitivity analyses.</p

Treatment distribution by risk groups.

<p><b>+</b> multimodal treatment.</p><p>Treatment distribution by risk groups.</p

Health state transition probabilities.

<p>PCa – prostate cancer,+multimodal treatment, P<i>_{mCRPC -}</i> probability of metastatic castrate resistant prostate cancer, P<i>_{overall death}</i> - probability of overall death, P<i>_recurrence</i> - probability of cancer recurrence, P<i>_{active tx}</i> – probability of active treatment.</p><p>Health state transition probabilities.</p

Sensitivity analyses.

<p>PCa – prostate cancer, mCRPC – metastatic castrate resistant prostate cancer, 95%CI –95% confidence interval, Sensitivity analysis I - primary androgen deprivation therapy received by low-risk cohort; Sensitivity analysis II - varied rates of active surveillance/treatments for base case; Sensitivity analysis III - varied rates of PCa recurrence/non-recurrence for base case; Sensitivity analysis IV - varied rate of PCa death for base case; Sensitivity analysis V - varied rate of overall death for base case.</p><p>Sensitivity analyses.</p