Hierarchical Changepoint Models for Biochemical Markers Illustratedby Tracking Postradiotherapy Prostate-Specific Antigen Series inMen With Prostate

PURPOSE: Biomarkers provide valuable information when detecting disease onset or monitoring diseaseprogression; examples include bone mineral density (for osteoporosis), cholesterol (for coronary artery diseases), or prostate-specific antigens (PSA, for prostate cancer). Characteristics of markers series can then beused as prognostic factors of disease progression, such as the postradiotherapy PSA doubling time in mentreated for prostate cancer. The statistical analysis of such data has to incorporate the within and be-tween-series variabilities, the complex patterns of the series over time, the unbalanced format of thedata, and the possibly nonconstant precision of the measurements.

METHODS: We base our analysis on a population-based cohort of 470 men treated with radiotherapy forprostate cancer; after treatment, the log2PSA concentrations follow a piecewise-linear pattern. We illustrate the flexibility of Bayesian hierarchical changepoint models by estimating the individual and popula-tion postradiotherapy log2PSA profiles; parameters such as the PSA nadir and the PSA doubling time wereestimated, and their associations with baseline patient characteristics were investigated. The residual PSA variability was modeled as a function of the PSA concentration. For comparison purposes, two alternativemodels were briefly considered.

RESULTS: Precise estimates of all parameters of the PSA trajectory are provided at both the individualand population levels. Estimates suggest greater PSA variability at lower PSA concentrations, as well as anassociation between shorter PSAdts and greater baseline PSA levels, higher Gleason scores, and older age.

CONCLUSIONS: The use of Bayesian hierarchical changepoint models accommodates multiple com-plex features of longitudinal data, permits realistic modeling of the variability as a function of the markerconcentration, and provides precise estimates of all clinically important parameters. This type of modelshould be applicable to the study of marker series in other diseases.

CARINE A. BELLERA,PHD, JAMES A. HANLEY,PHD, LAWRENCE JOSEPH,PHD,AND PETER C. ALBERTSEN,MD

PSA velocity does not aid long-term prediction of prostate cancer incidence

Elevated serum prostate-specific antigen (PSA) levels can indicate the presence of prostate cancer, although PSA levels are also elevated in some nonmalignant conditions, which affects the reliability of prostate cancer prediction. PSA levels rise sharply in patients with aggressive prostate cancer, and a recent study suggested that the rate of increase (PSA velocity) could predict life-threatening prostate cancer 10–15 years before diagnosis. Ulmert and colleagues evaluated data from the Malmö Preventative Medicine population-based study to compare the accuracy of a single PSA measurement versus PSA velocity in the long-term prediction of prostate cancer diagnosis.

Nature Clinical Practice Oncology (2008) 5, 302

Racial Differences in Prostate Cancer Screening by Family History

Purpose
Prostate cancer (CaP) is disproportionately prevalent among black, compared to white, men. Additionally, men with a family history of CaP have 75% to 80% higher risk of CaP. Therefore we examined racial variation in the association of family history of CaP and self-reported prostate-specific antigen (PSA) testing in the nationally-representative National Health Interview Survey (NHIS).

Methods
Data were obtained from the 2005 NHIS, including the Cancer Control Module supplement. We restricted the study sample to men over the age of 40 who reported having “ever heard of a PSA test” (N = 1,744). Men were considered to have a positive family history if either their biological father or at least one biological brother had been diagnosed with CaP. SUDAAN 9.0 was used to perform descriptive and multivariable logistic regression analyses.

Results
Men with a family history of CaP were more likely to have a PSA test than those who never had a PSA test (odds ratio [OR] = 1.8; 95% confidence interval [CI]: 1.3–2.5). Among blacks, men with a family history were not significantly more likely to have a PSA test.

Conclusions
Despite having the highest risk of cancer, black men with a family history are not screened more than black men without a family history.


Bettina F. Drake MPH, PhDa, Christopher S. Lathan MD, MPHb, Cassandra A. Okechukwu MSN, MPHc and Gary G. Bennett PhD

ARTICLE

Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome–disease relationships (QPDRs) and predict

In prostate cancer (PCa), prognostic (predictive) factors are particularly important given the marked heterogeneity of this disease at clinical, morphologic, and biomolecular levels. Blood contains a treasure of previously unstudied biomarkers that could reflect the ongoing physiological state of all tissue. The serum prostate-specific antigen (PSA) measurement is a very good biomarker for PCa, but the percentage of bad classification is somewhat high. The blood proteome mass spectra (MS) represent a potential tool for detection of diseases; however the identification of a single biomarker from the complex output from MS is often difficult. In this paper, we propose a general strategy, based on computational chemistry techniques, which should improve the predictive power of PSA. Our group adapted the square–spiral graph to represent human serum-plasma–proteome MS for healthy and PCa patients. These graphs were previously applied to DNA and/or protein sequences. In this work, we calculated different classes of connectivity indices (CIs), and created various models based on the spectral moments. The best QPDRs model found showed accuracy values ranging from 71.7% to 97.2%, and 70.4% to 99.2% of specificity. This methodology might be useful for several applications in computational chemistry.

Giulio Ferinoa, Humberto González-Díaza b, Giovanna Delogua, Gianni Poddaa, and Eugenio Uriarteb

ARTICLE