Abstract
Background: We previously identified a protein tumor signature of PTEN, SMAD4, SPP1, and CCND1 that, together with clinical features, was associated with lethal outcomes among prostate cancer patients. In the current study, we sought to validate the molecular model using time-dependent measures of AUC and predictive values for discriminating lethal from non-lethal prostate cancer.
Methods: Using data from the initial study, we fit survival models for men with prostate cancer who were participants in the Physicians’ Health Study (PHS; nā=ā276). Based on these models, we generated prognostic risk scores in an independent population, the Health Professionals Follow-up Study (HPFS; nā=ā347) to evaluate external validity. In each cohort, men were followed prospectively from cancer diagnosis through 2011 for development of distant metastasis or cancer mortality. We measured protein tumor expression of PTEN, SMAD4, SPP1, and CCND1 on tissue microarrays.
Results: During a median of 11.9 and 14.3 years follow-up in the PHS and HPFS cohorts, 24 and 32 men (9%) developed lethal disease. When used as a prognostic factor in a new population, addition of the four markers to clinical variables did not improve discriminatory accuracy through 15 years of follow-up.
Conclusions: Although the four markers have been identified as key biological mediators in metastatic progression, they do not provide independent, long-term prognostic information beyond clinical factors when measured at diagnosis. This finding may underscore the broad heterogeneity in aggressive prostate tumors and highlight the challenges that may result from overfitting in discovery-based research.
Published In | Prostate |
Date | Sep 7, 2015 |
DOI | 10.1002/pros.23090 |
Links |
Citation
Gerke TA*, Martin NE*, Ding Z, Nuttall E, Stack EC, Giovannucci EL, Lis RT, Stampfer MJ, Kantoff PW, Parmigiani G, Loda MF, Mucci LA. Evaluating a 4-marker signature of aggressive prostate cancer using time-dependent AUC. Prostate 2015; 75(16): 1926--1933. PMID: 26469352. PMCID: PMC4738177.