Dataset Information

Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.

ABSTRACT: Joint modeling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment-sensitive and treatment-resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients.

SUBMITTER: Desmee S

PROVIDER: S-EPMC5654727 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.

Desmée Solène S Mentré France F Veyrat-Follet Christine C Sébastien Bernard B Guedj Jérémie J

Biometrics 20160505 1

Joint modeling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prost ...[more]

PMID: 27148956

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Project description:Serum PSA, together with digital rectal examination and imaging of the prostate gland, have remained the gold standard in urological practices for the management of and intervention for prostate cancer. Based on these adopted practices, the limitations of serum PSA in identifying aggressive prostate cancer has led us to evaluate whether urinary PSA levels might have any clinical utility in prostate cancer diagnosis. Utilizing the Access Hybritech PSA assay, we evaluated a total of n = 437 urine specimens from post-DRE prostate cancer patients. In our initial cohort, PSA tests from a total of one hundred and forty-six (n = 146) urine specimens were obtained from patients with aggressive (Gleason Score ≥ 8, n = 76) and non-aggressive (Gleason Score = 6, n = 70) prostate cancer. A second cohort, with a larger set of n = 291 urine samples from patients with aggressive (GS ≥ 7, n = 168) and non-aggressive (GS = 6, n = 123) prostate cancer, was also utilized in our study. Our data demonstrated that patients with aggressive disease had lower levels of urinary PSA compared to the non-aggressive patients, while the serum PSA levels were higher in patients with aggressive prostate disease. The discordance between serum and urine PSA levels was further validated by immuno-histochemistry (IHC) assay in biopsied tumors and in metastatic lesions (n = 62). Our data demonstrated that aggressive prostate cancer was negatively correlated with the PSA in prostate cancer tissues, and, unlike serum PSA, urinary PSA might serve a better surrogate for capitulating tissue milieus to detect aggressive prostate cancer. We further explored the utility of urine PSA as a cancer biomarker, either alone and in combination with serum PSA, and their ratio (serum to urine PSA) to predict disease status. Comparing the AUCs for the urine and serum PSA alone, we found that urinary PSA had a higher predictive power (AUC= 0.732) in detecting aggressive disease. Furthermore, combining the ratios between serum to urine PSA with urine and serum assay enhanced the performance (AUC = 0.811) in predicting aggressive prostate disease. These studies support the role of urinary PSA in combination with serum for detecting aggressive prostate cancer.

Dataset Information

Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.

Publications

Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.

Similar Datasets

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