Objective To report the design and implementation of the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment Protocol that was developed to test the concept that prescribers can deliver genome guided therapy at the point-of-care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated in the electronic medical record (EMR). Patients and Methods We used a multivariable prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among Mayo Clinic Biobank participants with a recruitment goal of 1000 patients. Cox proportional hazards model was utilized using the variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. Results The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for ICD-9 codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 50% provided blood samples, 13% refused, 28% did not respond, and 9% consented but did not provide a blood sample within the recruitment window (October 4, 2012 – March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS is integrated in the EMR and flags potential patient-specific drug-gene interactions and provides therapeutic guidance. Conclusion These interventions will improve understanding and implementation of genomic data in clinical practice.

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PurposeDespite potential clinical benefits, implementation of pharmacogenomics (PGx) faces many technical and clinical challenges. These challenges can be overcome by a comprehensive and systematic implementation model.MethodsThe development and implementation of PGx was organized into eight interdependent components addressing resources, governance, clinical practice, education, testing, knowledge translation, clinical decision support (CDS) and maintenance. Several aspects of the implementation were assessed including adherence to the model, production of PGx-CDS interventions and access to educational resources.ResultsBetween 8/2012 and 6/2015, 21 specific drug-gene interactions were reviewed and 18 of them were implemented in the electronic medical record as PGx-CDS interventions. There was complete adherence to the model with variable production time (98 to 392 days) and delay time (0 to 148 days). The implementation impacted approximately 1247 unique providers and 3788 unique patients. A total of 11 educational resources complementary to the drug-gene interactions and 5 modules specific for pharmacists were developed and implemented.ConclusionA comprehensive operational model can support PGx implementation into routine prescribing. Institutions can use this model as a roadmap to support similar efforts. However, we also identified challenges that will require major multidisciplinary and multi-institutional efforts to make PGx a universal reality.

There is increasing recognition that genomic medicine as part of individualized medicine has a defined role in patient care. Rapid advances in technology and decreasing cost combine to bring genomic medicine closer to the clinical practice. There is also growing evidence that genomic-based medicine can advance patient outcomes, tailor therapy and decrease side effects. However the challenges to integrate genomics into the workflow involved in patient care remain vast, stalling assimilation of genomic medicine into mainstream medical practice. In this review we describe the approach taken by one institution to further individualize medicine by offering, executing and interpreting whole exome sequencing on a clinical basis through an enterprise-wide, standalone individualized medicine clinic. We present our experience designing and executing such an individualized medicine clinic, sharing lessons learned and describing early implementation outcomes.

Cytogenetic studies may provide important clues to the molecular pathogenesis of thyroid neoplasia. Thus, the authors attempted cytogenetic studies on 12 thyroid carcinomas: seven papillary, three follicular, and two anaplastic. Successful cytogenetic results were obtained on all 12 tumors; nine (75%) had one or more chromosomally abnormal clones. Four of the papillary carcinomas had a simple clonal karyotype, and three had no apparent chromosome abnormality. All four abnormal papillary tumors contained an anomaly of a chromosome 10q arm. In one instance, an inv(10)(q11.2q21.2) was observed in a Grade 2 papillary carcinoma as the sole acquired abnormality. In another case, an inversion or insertion involving 10q21.2 was found in a Grade 1 papillary tumor. The karyotype of a third tumor, a Grade 1 papillary carcinoma, was 46,XX,der(5)t(5;10)(p15.3;q11),der(9)t(9;?)(q11;?). A fourth abnormal papillary carcinoma, a Grade 1 tumor, had a t(6;10)(q21;q26.1) as the sole abnormality. Each of the five follicular or anaplastic carcinomas had a complex clonal karyotype. The three follicular carcinomas contained an abnormality of 3p25-p21, along with several other chromosome abnormalities.