Whole-Exome Sequencing of 10 Scientists: Evaluation of the Process and Outcomes.

OBJECTIVE: To understand motivations, educational needs, and concerns of individuals contemplating whole-exome sequencing (WES) and determine what amount of genetic information might be obtained by sequencing a generally healthy cohort so as to more effectively counsel future patients. PATIENTS AND METHODS: From 2012 to 2014, 40 medically educated, generally healthy scientists at Mayo Clinic were invited to have WES conducted on a research basis; 26 agreed to be in a drawing from which 10 participants were selected. The study involved pre- and posttest genetic counseling and completion of 4 surveys related to the experience and outcomes. Whole-exome sequencing was conducted on DNA from blood from each person. RESULTS: Most variants (76,305 per person; range, 74,505-77,387) were known benign allelic variants, variants in genes of unknown function, or variants of uncertain significance in genes of known function. The results of suspected pathogenic/pathogenic variants in Mendelian disorders and pharmacogenomic variants were disclosed. The mean number of suspected pathogenic/pathogenic variants was 2.2 per person (range, 1-4). Four pharmacogenomic genes were included for reporting; variants were found in 9 of 10 participants. CONCLUSION: This study provides data that may be useful in establishing reality-based patient expectations, outlines specific points to cover during counseling, and increases confidence in the feasibility of providing adequate preparation and counseling for WES in generally healthy individuals.

How well do whole exome sequencing results correlate with medical findings? A study of 89 Mayo Clinic Biobank samples.

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.

Implementing individualized medicine into the medical practice.

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.

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol.

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 into the electronic medical record (EMR). PATIENTS AND METHODS: We used a multivariate 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 the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with 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 International Classification of Diseases, Ninth Revision 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, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. CONCLUSION: This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.

The Mayo Clinic Biobank: a building block for individualized medicine.

OBJECTIVE: To report the design and implementation of the first 3 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 or older, 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 electronic medical record. A community advisory board provides ongoing advice and guidance on complex decisions. RESULTS: After 3 years of recruitment, 21,736 individuals have enrolled. Fifty-eight percent (12,498) of participants are female and 95% (20,541) of European ancestry. Median participant age is 62 years. Seventy-four percent (16,171) live in Minnesota, with 42% (9157) from Olmsted County, where the Mayo Clinic in Rochester, Minnesota, is located. The 5 most commonly self-reported conditions are hyperlipidemia (8979, 41%), hypertension (8174, 38%), osteoarthritis (6448, 30%), any cancer (6224, 29%), and gastroesophageal reflux disease (5669, 26%). Among patients with self-reported cancer, the 5 most common types are nonmelanoma skin cancer (2950, 14%), prostate cancer (1107, 12% in men), breast cancer (941, 4%), melanoma (692, 3%), and cervical cancer (240, 2% in women). Fifty-six percent (12,115) of participants have at least 15 years of electronic medical record history. To date, more than 60 projects and more than 69,000 samples have been approved for use. CONCLUSION: The Mayo Clinic Biobank has quickly been established as a valuable resource for researchers.

An unusual presentation of breast cancer in a very young woman.

A healthy 27-year-old woman presented to an outside institution with unilateral nipple pruritus and a slight brown discolouration. When her symptoms failed to respond to topical therapy, nipple biopsy revealed Paget's disease of the nipple. The patient sought further evaluation and care at our institution. Further investigation revealed multifocal breast cancer and the patient underwent bilateral mastectomies with sentinel lymph node biopsy followed by adjuvant systemic therapy. She is currently more than 4 years out from surgery and chemotherapy and has had no evidence of breast cancer recurrence.

Predicting BRCA1 and BRCA2 gene mutation carriers: comparison of PENN II model to previous study.

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 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.