Biobanks and archived datasets collecting samples and data have become crucial engines of genetic and genomic research. Unresolved, however, is what responsibilities biobanks should shoulder to manage incidental findings (IFs) and individual research results (IRRs) of potential health, reproductive, or personal importance to individual contributors (using “biobank” here to refer to both collections of samples and collections of data). This paper reports recommendations from a 2-year, NIH-funded project. The authors analyze responsibilities to manage return of IFs and IRRs in a biobank research system (primary research or collection sites, the biobank itself, and secondary research sites). They suggest that biobanks shoulder significant responsibility for seeing that the biobank research system addresses the return question explicitly. When re-identification of individual contributors is possible, the biobank should work to enable the biobank research system to discharge four core responsibilities: to (1) clarify the criteria for evaluating findings and roster of returnable findings, (2) analyze a particular finding in relation to this, (3) re-identify the individual contributor, and (4) recontact the contributor to offer the finding. The authors suggest that findings that are analytically valid, reveal an established and substantial risk of a serious health condition, and that are clinically actionable should generally be offered to consenting contributors. The paper specifies 10 concrete recommendations, addressing new biobanks and biobanks already in existence.

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.

Engaging patients and the public with evidence is an ethical imperative because engagement is central to respect for persons and will likely improve health outcomes, facilitate the stewardship of resources, enhance prospects for justice, and build public trust. However, patient and public engagement is also morally complex, because evidence alone is never definitive. As patients and the public engage with evidence, value conflicts will arise and must be managed to achieve trustworthy decision making. We outline value conflicts likely to emerge in the following five settings: clinical care, health care organizations, public health, the regulatory context, and among payers. Using a variety of examples, we offer suggestions about how such conflicts may be managed, including providing more opportunities for democratic deliberation and having more explicit community discussion of how to balance personal choice and community well-being, transparent discussions of cost and quality outcomes, and greater patient engagement in community-based participatory research and the governance of learning health systems.

IRBs must balance the need to recruit pediatric research subjects against the risk of undue influence during the recruitment process. Federal guidelines and expert pediatric opinion differ in recommendations regarding payment; responding IRBs appeared to follow federal guidelines more closely than guidelines proposed by the American Academy of Pediatrics.