An Approach To Correlate the Temporal Information To Facilitate Specimen Selection in the Breast Cancer Research Project.
Authors: Kvecher L. , Wu W. , Hooke J. , Shriver C. , Mural R. , Hu H. 
Published: December 2010

Abstract
Background: Temporal information management is very important in translational research. In the Clinical Breast Care Project (CBCP), the information on subjects and their specimens may be collected at multiple time points using multiple instruments.All such information is stored in an in-house data warehouse. Currently, 4000+ subjects have been enrolled in the study following HIPAA-compliant IRB-approved protocols with 35,000+ specimens collected. Some of the patient's information is static but other data are time dependent. As a result, selecting samples for experiments is a challenge due to complicated temporal relationships between samples and information collected through various instruments.Methods and Results: In the CBCP, the clinical information, blood, and solid tissues of a subject may be collected at multiple time points, associated with the completion of a Core Questionnaire (CQ) for clinical information, and/or a Pathology Checklist (PC) for pathology and sample information. We have designed and implemented an algorithm to use a set of pre-defined flags to precisely describe each sample related to patient's clinical and pathology information in the temporal domain. Five categories (flags) were created to describe the relationship between the sample date (SD) and the CQ date based on whether SD is within 60 days of the CQ date or there is missing data or not. The relationship between blood samples and pathology information is more complicated. Within 90 days, any of the 15 surgical procedures might be performed on a patient and blood samples might be collected before, at the time of, or between any procedures. For some experiments, it is crucial to select blood samples taken before tumor is impacted or severely impacted. Thus, we defined a dozen categories to describe the relationship between the SD and the procedure date (PD), including when the SD is earlier than any PD, equals to the first PD, or between certain procedures. Using these flags we have characterized the relationships between SDs and CQ dates, and between SDs and PDs for all the samples and all the subjects, and stored all the information into two relational tables. The temporal criteria for sample selection are now represented by the relationships between these flags, and can be implemented through several filtering processes. The described algorithm drastically reduces the time needed for precise sample selection from several days for manual efforts to several hours.Discussion: We are in the process of developing a general data model for temporal information management. The method described here is a transitional solution that fulfills our current needs. As an initial effort some of the thresholds for categorizing different temporal conditions are arbitrary, and we are validating them with experimental results for future improvement. Nonetheless, this algorithm has greatly enhanced the efficiency of our subject and specimen selection for wet bench experiments. The same principle can be applied to the future temporal data model solution, for CBCP and other human disease studies.
Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4173.