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EXECUTIVE SUMMARY1. BACKGROUND. A reliable system to control the quality of procurement instruments and to provide useful management information regarding document quality is needed within the Army Materiel Development and Readiness Command (DARCOM). Therefore, the Army Procurement Research Office (APRO) was tasked to develop and test a quality improvement system for procurement instruments that satisfies both requirements. A previous APRO report (613-1) presented the initial system design; this final report covers the test findings and resulting recommendations.2. STUDY OBJECTIVES. The objectives of this study are to develop and test a system that (i) aids in the improvement and control of the quality of DARCOM procurement instruments and (ii) provides useful management information compatible with the DARCOM procurement management program.3. STUDY APPROACH. The approach taken to achieve these objectives includes the identification and evaluation of quality indicators, a review and analysis of existing software quality control systems and techniques, interviews with procurement personnel, and the synthesis and test of a system that meets DARCOM's requirements.

We identified 296 patients with T1/T2 N0 prostate adenocarcinoma who underwent RP. Median age was 60 years with a median followup of 2.3 years. A total of 59% (nZ177) of patients had Gleason 7 disease and 54% (nZ154) had T1 disease. In addition, 40.8% (nZ121) of patients had biochemical failure following RP. After adjusting for clinical covariates, 4 genes were associated with differences in biochemical failure (Table 1). Specifically, increased RNA expression of E2F3 (hazard ratio [HR] 1.11, P<.001), CTNNB1 (HR 1.49, PZ.002), and AR (HR 1.21, P<.001) were seen in patients who experienced biochemical failure. Expression of TP53 (HR 0.75, p<.001) and Gleason 7 disease (HR 0.62 vs Gleason 8-10, P<.001) were associated with decreased likelihood of biochemical failure. Clustering by pathologic T stage accounted for 10% of variance in biochemical failure indicating that the 4 genes add important information to the prediction of biochemical recurrence. Conclusion: Genomic data augments pretreatment clinical data as well as pathologic staging data in the stratification of patients at risk for biochemical failure following RP. RNA expression of E2F3, CTNNB1, AR, and TP53 is associated with differences in biochemical failure following RP. Clinically, these genes could aid in identifying patients who may benefit from more regionally directed pelvic and prostate radiation therapy. Further studies of the relationship between these genes and the development of prostate cancer could aid in future targeted therapies.

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