Clinical Annotations for Prostate Cancer Research: Defining Data Elements, Creating a Reproducible Analytical Pipeline, and Assessing Data Quality

Background: Routine clinical data from clinical charts are indispensable for retrospective and prospective observational studies and clinical trials. Their reproducibility is often not assessed. Objective: To develop a prostate cancer-specific database with a defined source hierarchy for clinical annotations in conjunction with molecular profiling and to evaluate data reproducibility. Design, setting, and participants: For men with prostate cancer and clinical-grade paired tumor-normal sequencing, we performed team-based retrospective data collection from the electronic medical record at a comprehensive cancer center. We developed an open-source R package for data processing. We assessed reproducibility using blinded repeat annotation by a reference medical oncologist. Outcome measurements and statistical analysis: We evaluated completeness of data elements, reproducibility of team-based annotation compared to the reference, and impact of measurement error on bias in survival analyses. Results and limitations: Data elements on demographics, diagnosis and staging, disease state at the time of procuring a genomically characterized sample, and clinical outcomes were piloted and then abstracted for 2,261 patients (with 2,631 samples). Completeness of data elements was generally high. Comparing to the repeat annotation by a medical oncologist blinded to the database (100 patients/samples), reproducibility of annotations was high to very high; T stage, metastasis date, and presence and date of castration resistance had lower reproducibility. Impact of measurement error on estimates for strong prognostic factors was modest. Conclusions: With a prostate cancer-specific data dictionary and quality control measures, manual clinical annotations by a multidisciplinary team can be scalable and reproducible. The data dictionary and the R package for reproducible data processing are freely available to increase data quality in clinical prostate cancer research.

Pharmacoepidemiological Evaluation in Prostate Cancer—Common Pitfalls and How to Avoid Them

Pharmacoepidemiologic research provides opportunities to evaluate how commonly used drug groups, such as cholesterol-lowering or antidiabetic drugs, may affect the prostate cancer risk or mortality. This type of research is valuable in estimating real-life drug effects. Nonetheless, pharmacoepidemiological studies are prone to multiple sources of bias that mainly arise from systematic differences between medication users and non-users. If these are not appreciated and properly controlled for, there is a risk of obtaining biased results and reaching erroneous conclusions. Therefore, in order to improve the quality of future research, we describe common biases in pharmacoepidemiological studies, particularly in the context of prostate cancer research. We also list common ways to mitigate these biases and to estimate causality between medication use and cancer outcomes.

Prostate Specific Antigen (PSA): The Historical Perspective

In 1970, shortly after joining Roswell Park Memorial Institute, the New York State institute for the study of malignant diseases, the author initiated investigations on the use of tumor cell products for diagnosis and therapy of cancer. Immunochemical approaches were used primarily to differentiate quantitatively or qualitatively normal cells from tumor cells. Prostate cancer was a major area of endeavor, with the goal to identify and characterize prostate tumor specific and associated antigens, and eventually to develop a simple but reliable blood test for prostate cancer. Prostate cancer research had not received much attention at the time this work was begun. The early studies focused upon, among others, prostatic acid phosphatase, alkaline phosphatase, and new prostate tumor markers. By the mid 1970s, three able investigators--Dr. Ching-Li Lee, Dr. Carl S. Killian, and Dr. Ming C. Wang--had joined the prostate cancer research team, and were invited to take charge of these three research projects, respectively.