Pragmatic precision oncology: the secondary uses of clinical tumor molecular profiling.

BACKGROUND: Precision oncology increasingly utilizes molecular profiling of tumors to determine treatment decisions with targeted therapeutics. The molecular profiling data is valuable in the treatment of individual patients as well as for multiple secondary uses. OBJECTIVE: To automatically parse, categorize, and aggregate clinical molecular profile data generated during cancer care as well as use this data to address multiple secondary use cases. METHODS: A system to parse, categorize and aggregate molecular profile data was created. A naÿve Bayesian classifier categorized results according to clinical groups. The accuracy of these systems were validated against a published expertly-curated subset of molecular profiling data. RESULTS: Following one year of operation, 819 samples have been accurately parsed and categorized to generate a data repository of 10,620 genetic variants. The database has been used for operational, clinical trial, and discovery science research. CONCLUSIONS: A real-time database of molecular profiling data is a pragmatic solution to several knowledge management problems in the practice and science of precision oncology.

Implementing and Improving Automated Electronic Tumor Molecular Profiling.

Oncology practice increasingly requires the use of molecular profiling of tumors to inform the use of targeted therapeutics. However, many oncologists use third-party laboratories to perform tumor genomic testing, and these laboratories may not have electronic interfaces with the provider's electronic medical record (EMR) system. The resultant reporting mechanisms, such as plain-paper faxing, can reduce report fidelity, slow down reporting procedures for a physician's practice, and make reports less accessible. Vanderbilt University Medical Center and its genomic laboratory testing partner have collaborated to create an automated electronic reporting system that incorporates genetic testing results directly into the clinical EMR. This system was iteratively tested, and causes of failure were discovered and addressed. Most errors were attributable to data entry or typographical errors that made reports unable to be linked to the correct patient in the EMR. By providing direct feedback to providers, we were able to significantly decrease the rate of transmission errors (from 6.29% to 3.84%; P < .001). The results and lessons of 1 year of using the system and transmitting 832 tumor genomic testing reports are reported.