This application addresses the most critical conundrum in the management of prostate cancer, the most com- mon cancer after skin cancer in US men. The widespread adoption of PSA screening in the US during the 1990s has created an epidemic of low-risk prostate cancer in this country. The vast majority of low-risk cases will not die of their disease yet they continue to seek curative treatment. Active surveillance (AS) has been en- dorsed by an authoritative panel as a viable option for managing low-risk prostate cancer, but no evidence- based standard exists for how to implement it. In AS, cases are closely monitored and intent to treat if there is any evidence of disease progression. Several AS studies are ongoing but they constitute a loose collection of different approaches and their results cannot be readily compared or integrated. The overarching objective of this application is to determine an optimal approach to AS given patient characteristics and preferences. Rec- ognizing that the many potential approaches to AS cannot be compared in clinical trials, we will draw on our extensive experience modeling prostate cancer progression and prognosis to project the short- and long-term outcomes of candidate AS strategies. The model will build on a meta-analysis of disease progression across 5 of the highest quality and largest AS studies. This will be complemented by an independent model of disease progression in the absence of treatment that will adapt an existing, population-based model of prostate cancer natural history to the AS setting. We will superimpose candidate AS strategies and link the progression and treatment events with post-treatment survival and quality of life based on published studies of relapse, morbidi- ty, and utilities following primary treatment. The resulting model will project a range of outcomes, including the treatment-free interval, disease-specific survival, and quality-adjusted life years. The outcomes will be pack- aged in an interface that patients can use to compare outcomes of different strategies tailored to their individu- al characteristics and preferences.
Our Specific Aims are:
Aim 1 : Model prostate cancer progression in the absence of treatment among low-risk prostate cancer cases who are candidates for AS. As part of this aim we will conduct the first meta-analysis of ongoing AS studies.
Aim 2 : Project and compare short- and long-term outcomes on various AS protocols, using the models developed in Aim 1 along with survival and quality of life information from prior studies of AS and curative treatments for localized disease.
Aim 3 : Develop, test and release an interface using these models to help patients identify a personalized AS approach based on their clinical and pathologic characteristics at the time of diagnosis, their health status, and personal preferences. This application will advance the evidence base for AS and will be the first time AS strategies have been com- pared using the same metrics within a coherent medical decision-making framework. The main impact of the work lies in its potential to inform patient decision-making so that AS becomes more accepted and improves quality-adjusted life expectancy for the many men diagnosed low-risk prostate cancer each year in the US.
This project aims to improve our understanding of active surveillance, an approach to managing newly- diagnosed low-risk prostate cancers that monitors disease closely and only treats tumors that appear to be progressing. We will analyze data from several active surveillance studies to assess how likely it is that patients who choose active surveillance will eventually be treated and how likely it is that their disease will progres and become incurable. We will use this information to develop tools for patient decision making so that patients with low-risk prostate cancer can confidently choose less invasive options for managing their disease, thereby reducing overtreatment and the economic burden of cancer while increasing patient satisfaction.
|Ankerst, Donna P; Xia, Jing; Thompson Jr, Ian M et al. (2015) Precision Medicine in Active Surveillance for Prostate Cancer: Development of the Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator. Eur Urol 68:1083-8|
|Ankerst, Donna P; Pollock, Brad H; Liang, Yuanyuan et al. (2011) Trends and co-trends of prostate-specific antigen and body mass index in a screened population. Urology 78:10-6|