Kiley J. Johnson

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.

Functional Assays for Classification of BRCA2 Variants of Uncertain Significance

The assessment of the influence of many rare BRCA2 missense mutations on cancer risk has proved difficult. A multifactorial likelihood model that predicts the odds of cancer causality for missense variants is effective, but is limited by the availability of family data. As an alternative, we developed functional assays that measure the influence of missense mutations on the ability of BRCA2 to repair DNA damage by homologous recombination and to control centriole amplification. We evaluated 22 missense mutations from the BRCA2 DNA binding domain (DBD) that were identified in multiple breast cancer families using these assays and compared the results with those from the likelihood model. Thirteen variants inactivated BRCA2 function in at least one assay; two others truncated BRCA2 by aberrant splicing; and seven had no effect on BRCA2 function. Of 10 variants with odds in favor of causality in the likelihood model of 50:1 or more and a posterior probability of pathogenicity of 0.99, eight inactivated BRCA2 function and the other two caused splicing defects. Four variants and four controls displaying odds in favor of neutrality of 50:1 and posterior probabilities of pathogenicity of at least 1 x 10(-3) had no effect on function in either assay. The strong correlation between the functional assays and likelihood model data suggests that these functional assays are an excellent method for identifying inactivating missense mutations in the BRCA2 DBD and that the assays may be a useful addition to models that predict the likelihood of cancer in carriers of missense mutations.

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.

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.

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.

Genomic Medicine and Incidental Findings: Balancing Actionability and Patient Autonomy

M.J.B., ter for Mayo .S.P.). I n March 2013, the American College of Medical Genetics and Genomics (ACMG) released recommendations on how to handle incidental findings (IFs) for the clinical application of whole exome or whole genome sequencing (WES/WGS). The ACMG recommended that clinical laboratories “actively search,” evaluate, and report pathogenic or likely pathogenic variants in 56 genes and report these findings to the ordering clinician,who could then “contextualize any incidental findings for the patient in light of personal and family history, physical examination, and other relevant findings.” The 2013 recommendations did not provide guidance for laboratories to offer patients of any age the ability to opt out from the reporting of IFs. The 56 genes are associated with 24 genetic cardiovascular disorders or predisposition to cancers for which confirmatory diagnostic approaches are available as well as some preventive or treatment measures that can be offered. The ACMG recommended further that those who did not agree to learn of these IFs could choose to forego the entire test. These recommendations generated much controversy, most of which focused on patients’ ability to opt out of receiving unwanted results. Illustrating its commitment to participating in broad, public discussion, the ACMGBoard of Directors surveyed its membership in early 2014 to ascertain how the members viewed the 2013 recommendations. In addition, the ACMG surveyed attendees of the 2014 ACMG annual meeting. The responses suggest that most members support allowing informed patients to opt out of receiving information about some or all IFs (presented at the 2014 annual ACMG business meeting). The ACMG also held open forums to discuss the recommendations, including one at each of the 2013 and

Preserving personal autonomy in a genomic testing era

To the Editor: We read with great interest the article titled “An Informatics Approach to Analyzing the Incidentalome” by Berg et al.1 whose strategy surely is the direction of the future for clinical whole-exome/whole-genome sequencing (WES/WGS), with a focus on automation and reduction of output of uninterpretable results. The Mayo Clinic Center for Individualized Medicine has also been developing an approach with similar bioinformatics strategies, but it differs in the approach to binning. This letter is written to highlight alternative ways to group disorders for purposes of providing clinical choices. Berg et al.1 used three bins to classify genes: Bin 1 (n = 161) genes, deemed to have clinical utility, would be returned to patients by default; Bin 2 genes, having clinical validity but no appreciable clinical utility, were further subdivided into Bin 2b (n = 1,798) and Bin 2c (n = 57) based on a likely increasing potential for harm due to “their potential to cause psychosocial harm if revealed unexpectedly to an inadequately prepared individual.” The genes in Bin 2c are mostly untreatable neurodegenerative disorders. The authors proposed that “the return of Bin 2 findings be carefully guided by an appropriately qualified clinician, in the context of that individual’s personal situation, or not reported at all.” Bin 3 contained all other genes whose roles in human disease are undefined. The authors underscored that the binning used in the study is provisional, expecting the final population of bins to change over time. Even among experts, differences of opinion exist regarding what types of incidental genetic results should be returned. In a pilot study, 16 specialists in clinical/molecular genetics, a group of early adopters of genomic medicine, were asked to score variants in 99 common conditions to return to the ordering physician if discovered incidentally through WES/ WGS.2 Results showed imperfect concordance across this relatively homogeneous group of specialists, suggesting to us not that one should try to reach consensus but that we should strive for better ways to let individuals make their own decisions. The past two decades have provided copious data confirming that there is wide individual variability in interest/desire to know one’s genetic risks. Initial concerns of psychological harm were, fortunately, mostly unfounded. A primary lesson learned was that well-prepared patients make decisions for themselves that they can live with comfortably. The Mayo Center for Individualized Medicine has been grappling with development of a strongly patient-centered approach to integration of WES/WGS into clinical practice. Our approach starts with a structured genetic counseling component. We have created tools to assist patients not just in understanding genetics but in being able to articulate and prioritize their personal values and goals for having WES/WGS. These tools evolved out of design-centric ideas from the Mayo Center for Innovation, feedback from a community advisory board of the Mayo Clinic Biobank, and a team of genetic specialists. Use of these tools in the counseling process leads more easily to concrete decisions by the patients regarding opting in/ opting out of learning certain types of information. To link discovered mutations with disease phenotypes, it is common practice to use standard ontologies, such as the OMIM gene numbers. However, there are problems associated with this, because many genes have multiple OMIM phenotype numbers (e.g., FBN1 has seven different phenotypes; several entries on Berg et al.’s lists are phenotype numbers), and many OMIM phenotypes (e.g., retinitis pigmentosa) have multiple genes associated with them. We have developed an in-house system called the Tailored Result Selection Tool (TRuST) for 3,753 phenotypes (3,158 different phenotypes) that includes 2,478 different genes (and growing). All genes/ phenotypes are scored across multiple domains that include a score for “actionability,” which our work suggests is the concept that most resonates with people who are deciding about opting in or opting out of results. We do not find that actionability can be meaningfully distilled down to a “yes or no” choice, but rather it occupies a continuum ranging from disorders that are partially preventable or treatable to those diseases for which there is no prevention or treatment available. We do think that with adequate pretest counseling and education, individuals can make good informed decisions on this continuum and that this is preferable to a scenario in which there is no opt-out choice. No one can disagree that knowing of a BRCA1 mutation can lead to many constructive responses, but we all know that there are insightful individuals who, for whatever reason at that moment in their lives, say “Thanks, but no thanks.” Preserving that choice is important still. In addition to using TRuST guidance for framing clinical opt-in/opt-out choices, this system may be useful to researchers and biobanks as they question which results should be offered back to participants and struggle to balance the effort to contact participants with “actionability” and participant expectations. A 1–10 scale was used initially for the TRuST actionability scores to describe how adequately medical intervention restored or protected optimal health. We compared the published bins1 with our scores, which are currently combined into four groups: Preserving personal autonomy in a genomic testing era

Prenatally diagnosed trisomy 20 mosaicism associated with arachnoid cyst of basal cistern

A 30-year-old Caucasian woman at 20 weeks 0/7 days’ gestation was referred for consultation after a routine ultrasound exam identified a cystic structure below the cerebellum. Neither a first trimester screen nor a second trimester screen had been performed. A detailed ultrasound exam revealed a cystic space inferior to the cerebellum consistent with a dilated fourth ventricle communicating with the cisterna magna. The cerebellum appeared intact with no evidence of hypoplasia of the vermis. The posterior horn of the lateral ventricles was prominent at 8 mm and the choroid plexus appeared to be dangling. The third ventricle was also dilated. The remaining fetal anatomy was normal including an echocardiogram. A subsequent ultrasound at 26 weeks’ gestation was performed. The third and fourth ventricles appeared more dilated and there was hypoplasia of the inferior vermis, consistent with a possible Dandy-Walker malformation. The lateral ventricles were dilated to 10 mm. A genetic amniocentesis revealed trisomy 20 (47,XY,+20) in 100% of cells (Table 1). A cordocentesis was then performed at the request of the parents and this indicated that all fetal blood cells counted were normal 46,XY (Table 1). At 33 weeks’ gestation, the ultrasound revealed a large midline, intracranial asymmetrical cystic space measuring 2.6 cm × 2.6 cm. Small kidneys and unilateral right pyelectasis of 10 mm were identified. At 37 weeks, 5 days’ gestation, the midline intracranial cyst measured 4.1 cm × 2.6 cm. The patient was induced secondary to severe preeclampsia and oligohydramnios. A male weighing 3600 g with Apgars of 8 and 9 was delivered. The baby was stable and was taken to the neonatal intensive care unit on room air for monitoring. After delivery, two tissue samples (umbilical cord and prepuce) were obtained from the infant and cultured for

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.

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.