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.

OBJECTIVE To report the design and first three years of enrollment of the Mayo Clinic Biobank. PATIENTS AND METHODS Preparations for this Biobank began with a 4-day Deliberative Community Engagement with local residents to obtain community input into the design and governance of the biobank. Recruitment, which began in April 2009, is ongoing with a target goal of 50,000. Any Mayo Clinic patient who is 18+ years, able to consent, and a US resident is eligible to participate. Each participant completes a health history questionnaire, provides a blood sample and allows access to existing tissue specimens and all data from their Mayo Clinic medical record (EMR). A Community Advisory Board provides ongoing advice and guidance on complex decisions. RESULTS After three years of recruitment, 21,736 subjects have enrolled. Participants were 58% female, 95% of European ancestry, and median age of 62 years. Seventy-four percent lived in Minnesota, 42% from Olmsted County where the Mayo Clinic Rochester is located. The five most commonly self-reported conditions were hyperlipidemia (41%), hypertension (38%), osteoarthritis (30%), any cancer (29%), and gastroesophageal reflux disease (26%). Among self-reported cancer patients, the five most common types were non-melanoma skin cancer (14%), prostate cancer (12% in men), breast cancer (4%), melanoma (3%), and cervical cancer (2% in women). Fifty-six percent of participants had at least 15 years of EMR history. To date, over sixty projects and over 69,000 samples have been approved for use. CONCLUSION The Mayo Clinic Biobank has quickly been established as a valuable resource for researchers.

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.

A number of models have been developed to predict the probability that a person carries a detectable germline mutation in the BRCA1 or BRCA2 genes. Their relative performance in a clinical setting is variable. To compare the performance characteristics of a web-based BRCA1/BRCA2 gene mutation prediction model: the PENNII model (www.afcri.upenn.edu/itacc/penn2), with studies done previously at our institution using four other models including LAMBDA, BRCAPRO, modified PENNI (Couch) tables, and Myriad II tables collated by Myriad Genetics Laboratories. Proband and © Springer Science+Business Media B.V. 2010Correspondence to: Noralane M. Lindor, nlindor@mayo.edu. NIH Public Access Author ManuscriptFam Cancer. Author manuscript; available in PMC 2011 December 1. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript family cancer history data were analyzed from 285 probands from unique families (27 Ashkenazi Jewish; 277 female) seen for genetic risk assessment in a multispecialty tertiary care group practice. All probands had clinical testing for BR.CA1 and BRCA2 mutations conducted in the same single commercial laboratory. The performance for PENNII results were assessed by the area under the receiver operating characteristic curve (AUC) of sensitivity versus 1-specificity, as a measure of ranking. The AUCs of the PENNII model were higher for predicting BRCA1 than for BRCA2 (81 versus 72%). The overall AUC was 78.7%. PENN II model for BRCA1/2 prediction performed well in this population with higher AUC compared with our experience using four other models. The ease of use of the PENNII model is compatible with busy clinical practices.

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Whole exome sequencing (WES) is increasingly being used for diagnosis without adequate information on predictive characteristics of reportable variants typically found on any given individual and correlation with clinical phenotype. In this study, we performed WES on 89 deceased individuals (mean age at death 74 years, range 28–93) from the Mayo Clinic Biobank. Significant clinical diagnoses were abstracted from electronic medical record via chart review. Variants [Single Nucleotide Variant (SNV) and insertion/deletion] were filtered based on quality (accuracy >99%, read-depth >20, alternate-allele read-depth >5, minor-allele-frequency <0.1) and available HGMD/OMIM phenotype information. Variants were defined as Tier-1 (nonsense, splice or frame-shifting) and Tier-2 (missense, predicted-damaging) and evaluated in 56 ACMG-reportable genes, 57 cancer-predisposition genes, along with examining overall genotype–phenotype correlations. Following variant filtering, 7046 total variants were identified (~79/person, 644 Tier-1, 6402 Tier-2), 161 among 56 ACMG-reportable genes (~1.8/person, 13 Tier-1, 148 Tier-2), and 115 among 57 cancer-predisposition genes (~1.3/person, 3 Tier-1, 112 Tier-2). The number of variants across 57 cancer-predisposition genes did not differentiate individuals with/without invasive cancer history (P > 0.19). Evaluating genotype–phenotype correlations across the exome, 202(3%) of 7046 filtered variants had some evidence for phenotypic correlation in medical records, while 3710(53%) variants had no phenotypic correlation. The phenotype associated with the remaining 44% could not be assessed from a typical medical record review. These data highlight significant continued challenges in the ability to extract medically meaningful predictive results from WES.

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