A formal risk-benefit framework for genomic tests: facilitating the appropriate translation of genomics into clinical practice

Methodological guidelines and publications of benefit-risk assessment for health technology assessment: a scoping review

OBJECTIVES

To map the available methodological guidelines and documents for conducting and reporting benefit-risk assessment (BRA) during health technologies' life cycle; and to identify methodological guidelines for BRA that could serve as the basis for the development of a BRA guideline for the context of health technology assessment (HTA) in Brazil.

DESIGN

Scoping review.

METHODS

Searches were conducted in three main sources up to March 2023: (1) electronic databases; (2) grey literature (48 HTA and regulatory organisations) and (3) manual search and contacting experts. We included methodological guidelines or publications presenting methods for conducting or reporting BRA of any type of health technologies in any context of the technology's life cycle. Selection process and data charting were conducted by independent reviewers. We provided a structured narrative synthesis of the findings.

RESULTS

From the 83 eligible documents, six were produced in the HTA context, 30 in the regulatory and 35 involved guidance for BRA throughout the technology's life cycle. We identified 129 methodological approaches for BRA in the documents. The most commonly referred to descriptive frameworks were the Problem, Objectives, Alternatives, Consequences, Trade-offs, Uncertainty, Risk and Linked decisions and the Benefit-Risk Action Team. Multicriteria decision analysis was the most commonly cited quantitative framework. We also identified the most cited metric indices, estimation and utility survey techniques that could be used for BRA.

CONCLUSIONS

Methods for BRA in HTA are less established. The findings of this review, however, will support and inform the elaboration of the Brazilian methodological guideline on BRA for HTA.

TRIAL REGISTRATION NUMBER

https://doi.org/10.17605/OSF.IO/69T3V.

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.

Polygenic Health Index, General Health, and Pleiotropy: Sibling Analysis and Disease Risk Reduction

We construct a polygenic health index as a weighted sum of polygenic risk scores for 20 major disease conditions, including, e.g., coronary artery disease, type 1 and 2 diabetes, schizophrenia, etc. Individual weights are determined by population-level estimates of impact on life expectancy. We validate this index in odds ratios and selection experiments using unrelated individuals and siblings (pairs and trios) from the UK Biobank. Individuals with higher index scores have decreased disease risk across almost all 20 diseases (no significant risk increases), and longer calculated life expectancy. When estimated Disability Adjusted Life Years (DALYs) are used as the performance metric, the gain from selection among ten individuals (highest index score vs average) is found to be roughly 4 DALYs. We find no statistical evidence for antagonistic trade-offs in risk reduction across these diseases. Correlations between genetic disease risks are found to be mostly positive and generally mild. These results have important implications for public health and also for fundamental issues such as pleiotropy and genetic architecture of human disease conditions.

Structured approaches to implementation of clinical genomics: A scoping review

PURPOSE

This study aimed to assess the extent to which structured approaches to implementation of clinical genomics, proposed or adapted, are informed by evidence.

METHODS

A systematic approach was used to identify peer-reviewed articles and gray literature to report on 4 research questions: 1. What structured approaches have been proposed to support implementation? 2. To what extent are the structured approaches informed by evidence? 3. How have structured approaches been deployed in the genomic setting? 4. What are the intended outcomes of the structured approaches?

RESULTS

A total of 30 unique structured approaches to implementation were reported across 23 peer-reviewed publications and 11 gray literature articles. Most approaches were process models, applied in the preadoption implementation phase, focusing on a "service" outcome. Key findings included a lack of implementation science theory informing the development/implementation of newly designed structured approaches in the genomic setting and a lack of measures to assess implementation effectiveness.

CONCLUSION

This scoping review identified a significant number of structured approaches developed to inform the implementation of genomic medicine into clinical practice, with limited use of implementation science to support the process. We recommend the use of existing implementation science theory and the expertise of implementation scientists to inform the design of genomic programs being implemented into clinical care.

Understanding innovation of health technology assessment methods: the IHTAM framework

Adequate methods are urgently needed to guarantee the good practice of health technology assessment (HTA) for technologies with novel properties. The aim of the study was to construct a conceptual framework to help understand the innovation of HTA methods (IHTAM). The construction of the IHTAM framework was based on two scoping reviews, one on the current practice of innovating methods, that is existing HTA frameworks, and one on theoretical foundations for innovating methods outside the HTA discipline. Both aimed to identify and synthesize concepts of innovation (i.e., innovation processes and roles of stakeholders in innovation). Using these concepts, the framework was developed in iterative brainstorming sessions and subsequent discussions with representatives from various stakeholder groups. The framework was constructed based on twenty documents on innovating HTA frameworks and fourteen guidelines from three scientific disciplines. It includes a generic innovation process consisting of three phases (“Identification,” “Development,” and “Implementation”) and nine subphases. In the framework, three roles that HTA stakeholders can play in innovation (“Developers,” “Practitioners,” and “Beneficiaries”) are defined, and a process on how the stakeholders innovate HTA methods is included. The IHTAM framework visualizes systematically which elements and stakeholders are important to the development and implementation of novel HTA methods. The framework could be used by all stakeholders involved in HTA innovation to learn how to engage dynamically and collaborate effectively throughout the innovation process. HTA stakeholders in practice have welcomed the framework, though additional testing of its applicability and acceptance is essential.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

Conceptualising 'Benefits Beyond Health' in the Context of the Quality-Adjusted Life-Year: A Critical Interpretive Synthesis

There is growing interest in extending the evaluative space of the quality-adjusted life-year framework beyond health. Using a critical interpretive synthesis approach, the objective was to review peer-reviewed literature that has discussed non-health outcomes within the context of quality-adjusted life-years and synthesise information into a thematic framework. Papers were identified through searches conducted in Web of Science, using forward citation searching. A critical interpretive synthesis allows for the development of interpretations (synthetic constructs) that go beyond those offered in the original sources. The final output of a critical interpretive synthesis is the synthesising argument, which integrates evidence from across studies into a coherent thematic framework. A concept map was developed to show the relationships between different types of non-health benefits. The critical interpretive synthesis was based on 99 papers. The thematic framework was constructed around four themes: (1) benefits affecting well-being (subjective well-being, psychological well-being, capability and empowerment); (2) benefits derived from the process of healthcare delivery; (3) benefits beyond the recipient of care (spillover effects, externalities, option value and distributional benefits); and (4) benefits beyond the healthcare sector. There is a wealth of research concerning non-health benefits and the evaluative space of the quality-adjusted life-year. Further dialogue and debate are necessary to address conceptual and normative challenges, to explore the societal willingness to sacrifice health for benefits beyond health and to consider the equity implications of different courses of action.

Conceptualization of utility in translational clinical genomics research

Prior to integration into clinical care, a novel medical innovation is typically assessed in terms of its balance of benefits and risks, often referred to as utility. Members of multidisciplinary research teams may conceptualize and assess utility in different ways, which has implications within the translational genomics community and for the evidence base upon which clinical guidelines groups and healthcare payers make decisions. Ambiguity in the conceptualization of utility in translational genomics research can lead to communication challenges within research teams and to study designs that do not meet stakeholder needs. We seek to address the ambiguity challenge by describing the conceptual understanding of utility and use of the term by scholars in the fields of philosophy, medicine, and the social sciences of decision psychology and health economics. We illustrate applications of each field's orientation to translational genomics research by using examples from the Clinical Sequencing Evidence-Generating Research (CSER) consortium, and we provide recommendations for increasing clarity and cohesion in future research. Given that different understandings of utility will align to a greater or lesser degree with important stakeholders' views, more precise use of the term can help researchers to better integrate multidisciplinary investigations and communicate with stakeholders.

Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations

Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and discuss related ethical considerations.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

Utility and Diversity: Challenges for Genomic Medicine

Genomic information is poised to play an increasing role in clinical care, extending beyond highly penetrant genetic conditions to less penetrant genotypes and common disorders. But with this shift, the question of clinical utility becomes a major challenge. A collaborative effort is necessary to determine the information needed to evaluate different uses of genomic information and then acquire that information. Another challenge must also be addressed if that process is to provide equitable benefits: the lack of diversity of genomic data. Current genomic knowledge comes primarily from populations of European descent, which poses the risk that most of the human population will be shortchanged when health benefits of genomics emerge. These two challenges have defined my career as a geneticist and have taught me that solutions must start with dialogue across disciplinary and social divides.

A Systematic Review of the Value Assessment Frameworks Used within Health Technology Assessment of Omics Technologies and Their Actual Adoption from HTA Agencies

BACKGROUND

Omics technologies, enabling the measurements of genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics), are valuable tools for personalized decision-making. We aimed to identify the existing value assessment frameworks used by health technology assessment (HTA) doers for the evaluation of omics technologies through a systematic review.

METHODS

PubMed, Scopus, Embase and Web of Science databases were searched to retrieve potential eligible articles published until 31 May 2020 in English. Additionally, through a desk research in HTA agencies' repositories, we retrieved the published reports on the practical use of these frameworks.

RESULTS

Twenty-three articles were included in the systematic review. Twenty-two frameworks, which addressed genetic and/or genomic technologies, were described. Most of them derived from the ACCE framework and evaluated the domains of analytical validity, clinical validity and clinical utility. We retrieved forty-five reports, which mainly addressed the commercial transcriptomic prognostics and next generation sequencing, and evaluated clinical effectiveness, economic aspects, and description and technical characteristics.

CONCLUSIONS

A value assessment framework for the HTA evaluation of omics technologies is not standardized and accepted, yet. Our work reports that the most evaluated domains are analytical validity, clinical validity and clinical utility and economic aspects.

Sibling validation of polygenic risk scores and complex trait prediction

We test 26 polygenic predictors using tens of thousands of genetic siblings from the UK Biobank (UKB), for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in between-sibling designs. In the case of disease risk we test the extent to which higher polygenic risk score (PRS) identifies the affected sibling, and also compute Relative Risk Reduction as a function of risk score threshold. For quantitative traits we examine between-sibling differences in trait values as a function of predicted differences, and compare to performance in non-sibling pairs. Example results: Given 1 sibling with normal-range PRS score (< 84 percentile, < + 1 SD) and 1 sibling with high PRS score (top few percentiles, i.e. > + 2 SD), the predictors identify the affected sibling about 70–90% of the time across a variety of disease conditions, including Breast Cancer, Heart Attack, Diabetes, etc. 55–65% of the time the higher PRS sibling is the case. For quantitative traits such as height, the predictor correctly identifies the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.

Genetic architecture of complex traits and disease risk predictors

Genomic prediction of complex human traits (e.g., height, cognitive ability, bone density) and disease risks (e.g., breast cancer, diabetes, heart disease, atrial fibrillation) has advanced considerably in recent years. Using data from the UK Biobank, predictors have been constructed using penalized algorithms that favor sparsity: i.e., which use as few genetic variants as possible. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. For the majority of disease conditions studied, a large amount of the variance is accounted for by SNPs outside of coding regions. The state of these SNPs cannot be determined from exome-sequencing data. This suggests that exome data alone will miss much of the heritability for these traits-i.e., existing PRS cannot be computed from exome data alone. We also study the fraction of SNPs and of variance that is in common between pairs of predictors. The DNA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated. It seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously.

[Blood metabolome analysis for creating a digital image of a healthy person]

In the frame of the work, data on the implementation of metabolomics tests in medicine have been systematized. Based on the obtained data, a set of protocols was proposed, the sequential realization of which makes it possible to conduct a blood metabolome analysis for medical purposes. Using this analysis and the number of blood samples from healthy volunteers, a prototype of a healthy person's metabolomic image has been developed; it allows visually and digitally to assess the compliance of the human blood metabolome with the norm. At the same time, 99% of the metabolic processes reflected in the blood plasma are estimated. If abnormalities are detected, the metabolomic image allows to get the value of these deviations of metabolic processes in digital terms.

Experiences and lessons learned by genetic counselors in returning secondary genetic findings to patients

Few studies have explored the real-world experiences and strategies of genetic counselors involved in the process of returning secondary findings (SFs). This study aimed to describe and categorize the experiences for the return of SFs from clinical sequencing. Semi-structured telephone interviews with 21 genetic counselors representing 56 incidences were conducted. A content analysis was conducted on the transcripts through an iterative, team-based approach. Four common categories emerged across all interviews. These included (a) the importance of pretest counseling for the return of SFs, (b) how primary test results influenced the level of importance placed on the SFs, (c) patients' emotional reactions from receiving SF results, and (d) how returning SFs changed future pretest counseling and consent. This study identified experiences and common practices by genetic counselors who returned SFs. More research is needed to assess how genetic counselors' specific strategies improve patient comprehension and medical actions.

Health Economics Tools and Precision Medicine: Opportunities and Challenges

Precision medicine - individualizing care for patients and addressing variations in treatment response - is likely to be important in improving the nation's health in a cost-effective manner. Despite this promise, widespread use of precision medicine, specifically genomic markers, in clinical care has been limited in practice to date. Lack of evidence, clear evidence thresholds, and reimbursement have been cited as major barriers. Health economics frameworks and tools can elucidate the effects of legal, regulatory, and reimbursement policies on the use of precision medicine while guiding research investments to enhance the appropriate use of precision medicine. Despite the capacity of economics to enhance the clinical and human impact of precision medicine, application of health economics to precision medicine has been limited - in part because precision medicine is a relatively new field - but also because precision medicine is complex, both in terms of its applications and implications throughout medicine and the healthcare system. The goals of this review are several-fold: (1) provide an overview of precision medicine and key policy challenges for the field; (2) explain the potential utility of economics methods in addressing these challenges; (3) describe recent research activities; and (4) summarize opportunities for cross-disciplinary research.

Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer

We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~0.58-0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

Generic Cost-Effectiveness Models: A Proof of Concept of a Tool for Informed Decision-Making for Public Health Precision Medicine

BACKGROUND/AIMS

Economic evaluation is integral to informed public health decision-making in the rapidly growing field of precision and personalized medicine (PM); however, this research requires specialized expertise and significant resources. Generic models are a novel innovation to efficiently address a critical PM evidence shortage and implementation barrier by enabling use of population-specific input values. This is a generic PM economic evaluation model proof-of-concept study for a pharmacogenomic use case.

METHODS

An 8-step generic economic model development process was applied to the use case of human leukocyte antigen (HLA)-B*15:02genotyping for prediction of carbamazepine-induced cutaneous reactions, with a user-friendly decision-making tool relying on user-provided input values. This generic model was transparently documented and validated, including cross-validation comparing cost-effectiveness results with 3 country-specific models.

RESULTS

A generic pharmacogenomic use case cost-effectiveness model with decision-making tool was successfully developed and cross-validated using input values for 6 populations which produced consistent results for HLA-B*15:02 screening at country-specific cost-effectiveness threshold values. Differences between the generic and country-specific model results were largely due to differences in model structure and assumptions.

CONCLUSION

This proof on concept demonstrates the feasibility of generic models to provide useful PM economic evidence, supporting their use as a pragmatic and timely approach to address a growing need.

Healthcare System Priorities for Successful Integration of Genomics: An Australian Focus

This paper examines key considerations for the successful integration of genomic technologies into healthcare systems. All healthcare systems strive to introduce new technologies that are effective and affordable, but genomics offers particular challenges, given the rapid evolution of the technology. In this context we frame internationally relevant discussion points relating to effective and sustainable implementation of genomic testing within the strategic priority areas of the recently endorsed Australian National Health Genomics Policy Framework. The priority areas are services, data, workforce, finances, and person-centred care. In addition, we outline recommendations from a government perspective through the lens of the Australian health system, and argue that resources should be allocated not to just genomic testing alone, but across the five strategic priority areas for full effectiveness.

Delivery Of Cascade Screening For Hereditary Conditions: A Scoping Review Of The Literature

Cascade screening is the process of contacting relatives of people who have been diagnosed with certain hereditary conditions. Its purpose is to identify, inform, and manage those who are also at risk. We conducted a scoping review to obtain a broad overview of cascade screening interventions, facilitators and barriers to their use, relevant policy considerations, and future research needs. We searched for relevant peer-reviewed literature in the period 1990-2017 and reviewed 122 studies. Finally, we described 45 statutes and regulations related to the use and release of genetic information across the fifty states. We sought standardized best practices for optimizing cascade screening across various geographic and policy contexts, but we found none. Studies in which trained providers contacted relatives directly, rather than through probands (index patients), showed greater cascade screening uptake; however, policies in some states might limit this approach. Major barriers to cascade screening delivery include suboptimal communication between the proband and family and geographic barriers to obtaining genetic services. Few US studies examined interventions for cascade screening or used rigorous study designs such as randomized controlled trials. Moving forward, there remains an urgent need to conduct rigorous intervention studies on cascade screening in diverse US populations, while accounting for state policy considerations.

Do Health Information Exchanges Deter Repetition of Medical Services?

Repetition of medical services by providers is one of the major sources of healthcare costs. The lack of access to previous medical information on a patient at the point of care often leads a physician to perform medical procedures that have already been done. Multiple healthcare initiatives and legislation at both the federal and state levels have mandated Health Information Exchange (HIE) systems to address this problem. This study aims to assess the extent to which HIE could reduce these repetitions, using data from Centers for Medicare 8 Medicaid Services and a regional HIE organization. A 2-Stage Least Square model is developed to predict the impact of HIE on repetitions of two classes of procedures: diagnostic and therapeutic. The first stage is a predictive analytic model that estimates the duration of tenure of each HIE member-practice. Based on these estimates, the second stage predicts the effect of providers’ HIE tenure on their repetition of medical services. The model incorporates moderating effects of a federal quality assurance program and the complexity of medical procedures with a set of control variables. Our analyses show that a practice's tenure with HIE significantly lowers the repetition of therapeutic medical procedures, while diagnostic procedures are not impacted. The medical reasons for the effects observed in each class of procedures are discussed. The results will inform healthcare policymakers and provide insights on the business models of HIE platforms.

Defining personal utility in genomics: A Delphi study

BACKGROUND

Individual genome sequencing results are valued by patients in ways distinct from clinical utility. Such outcomes have been described as components of "personal utility," a concept that broadly encompasses patient-endorsed benefits, that is operationally defined as non-clinical outcomes. No empirical delineation of these outcomes has been reported.

AIM

To address this gap, we administered a Delphi survey to adult participants in a National Institute of Health (NIH) clinical exome study to extract the most highly endorsed outcomes constituting personal utility.

MATERIALS AND METHODS

Forty research participants responded to a Delphi survey to rate 35 items identified by a systematic literature review of personal utility.

RESULTS

Two rounds of ranking resulted in 24 items that represented 14 distinct elements of personal utility. Elements most highly endorsed by participants were: increased self-knowledge, knowledge of "the condition," altruism, and anticipated coping.

DISCUSSION

Our findings represent the first systematic effort to delineate elements of personal utility that may be used to anticipate participant expectation and inform genetic counseling prior to sequencing. The 24 items reported need to be studied further in additional clinical genome sequencing studies to assess generalizability in other populations. Further research will help to understand motivations and to predict the meaning and use of results.

Personal utility in genomic testing: a systematic literature review

Researchers and clinicians refer to outcomes of genomic testing that extend beyond clinical utility as 'personal utility'. No systematic delineation of personal utility exists, making it challenging to appreciate its scope. Identifying empirical elements of personal utility reported in the literature offers an inventory that can be subsequently ranked for its relative value by those who have undergone genomic testing. A systematic review was conducted of the peer-reviewed literature reporting non-health-related outcomes of genomic testing from 1 January 2003 to 5 August 2016. Inclusion criteria specified English language, date of publication, and presence of empirical evidence. Identified outcomes were iteratively coded into unique domains. The search returned 551 abstracts from which 31 studies met the inclusion criteria. Study populations and type of genomic testing varied. Coding resulted in 15 distinct elements of personal utility, organized into three domains related to personal outcomes: affective, cognitive, and behavioral; and one domain related to social outcomes. The domains of personal utility may inform pre-test counseling by helping patients anticipate potential value of test results beyond clinical utility. Identified elements may also inform investigations into the prevalence and importance of personal utility to future test users.

Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association

The process of scientific discovery is rapidly evolving. The funding climate has influenced a favorable shift in scientific discovery toward the use of existing resources such as the electronic health record. The electronic health record enables long-term outlooks on human health and disease, in conjunction with multidimensional phenotypes that include laboratory data, images, vital signs, and other clinical information. Initial work has confirmed the utility of the electronic health record for understanding mechanisms and patterns of variability in disease susceptibility, disease evolution, and drug responses. The addition of biobanks and genomic data to the information contained in the electronic health record has been demonstrated. The purpose of this statement is to discuss the current challenges in and the potential for merging electronic health record data and genomics for cardiovascular research.

Systems biology approaches to adverse drug effects: the example of cardio-oncology

Increased awareness of the cardiovascular toxic effects of chemotherapy has led to the emergence of cardio-oncology (or onco-cardiology), which focuses on screening, monitoring and treatment of patients with cardiovascular dysfunctions resulting from chemotherapy. Anthracyclines, such as doxorubicin, and HER2 inhibitors, such as trastuzumab, both have cardiotoxic effects. The biological rationale, mechanisms of action and cardiotoxicity profiles of these two classes of drugs, however, are completely different, suggesting that cardiotoxic effects can occur in a range of different ways. Advances in genomics and proteomics have implicated several genomic variants and biological pathways that can influence the susceptibility to cardiotoxicity from these, and other drugs. Established pathways include multidrug resistance proteins, energy utilization pathways, oxidative stress, cytoskeletal regulation and apoptosis. Gene-expression profiles that have revealed perturbed pathways have vastly increased our knowledge of the complex processes involved in crosstalk between tumours and cardiac function. Utilization of mathematical and computational modelling can complement pharmacogenomics and improve individual patient outcomes. Such endeavours should enable identification of variations in cardiotoxicity, particularly in those patients who are at risk of not recovering, even with the institution of cardioprotective therapy. The application of systems biology holds substantial potential to advance our understanding of chemotherapy-induced cardiotoxicity.

The Human Genome Project, and recent advances in personalized genomics

The language of “personalized medicine” and “personal genomics” has now entered the common lexicon. The idea of personalized medicine is the integration of genomic risk assessment alongside other clinical investigations. Consistent with this approach, testing is delivered by health care professionals who are not medical geneticists, and where results represent risks, as opposed to clinical diagnosis of disease, to be interpreted alongside the entirety of a patient’s health and medical data. In this review we consider the evidence concerning the application of such personalized genomics within the context of population screening, and potential implications that arise from this. We highlight two general approaches which illustrate potential uses of genomic information in screening. The first is a narrowly targeted approach in which genetic profiling is linked with standard population-based screening for diseases; the second is a broader targeting of variants associated with multiple single gene disorders, performed opportunistically on patients being investigated for unrelated conditions. In doing so we consider the organization and evaluation of tests and services, the challenge of interpretation with less targeted testing, professional confidence, barriers in practice, and education needs. We conclude by discussing several issues pertinent to health policy, namely: avoiding the conflation of genetics with biological determinism, resisting the “technological imperative”, due consideration of the organization of screening services, the need for professional education, as well as informed decision making and public understanding.

Pharmacogenomic biomarkers for personalized cancer treatment

Personalized medicine involves the selection of the safest and most effective pharmacological treatment based on the molecular characteristics of the patient. In the case of anticancer drugs, tumour cell alterations can have a great impact on drug activity and, in fact, most biomarkers predicting response originate from these cells. On the other hand, the risk of developing severe toxicity may be related to the genetic background of the patient. Thus, understanding the molecular characteristics of both the tumour and the patient, and establishing their relation with drug outcomes will be critical for the identification of predictive biomarkers and to provide the basis for individualized treatments. This is a complex scenario where multiple genes as well as pathophysiological and environmental factors are important; in addition, tumours exhibit large inter- and intraindividual variability in space and time. Against this background, the huge amounts of biological and genetic data generated by the high-throughput technologies will facilitate pharmacogenomic progress, suggest novel druggable molecules and support the design of future strategies aimed at disease control. Here, we will review the current challenges and opportunities for pharmacogenomic studies in oncology, as well as the clinically established biomarkers. Lung and renal cancer, two areas in which huge progress has been made in the last decade, will be used to illustrate advances in personalized cancer treatment; we will review EGFR mutation as the paradigm of targeted therapies in lung cancer, and discuss the dissection of lung cancer into clinically relevant molecular subsets and novel advances that suggest an important role of single nucleotide polymorphisms in the response to antiangiogenic agents, as well as the challenges that remain in these fields. Finally, we will present new approaches and future prospects for personalizing medicine in oncology.

Capturing the clinical utility of genomic testing: medical recommendations following pediatric microarray

Interpretation of pediatric chromosome microarray (CMA) results presents diagnostic and medical management challenges. Understanding management practices triggered by CMA will inform clinical utility and resource planning. Using a retrospective cohort design, we extracted clinical and management-related data from the records of 752 children with congenital anomalies and/or developmental delay who underwent CMA in an academic pediatric genetics clinic (2009–2011). Frequency distributions and relative rates (RR) of post-CMA medical recommendations in children with reportable and benign CMA results were calculated. Medical recommendations were provided for 79.6% of children with reportable results and 62.0% of children with benign results. Overall, recommendations included specialist consultation (40.8%), imaging (32.5%), laboratory investigations (17.2%), surveillance (4.6%), and family investigations (4.9%). Clinically significant variants and variants of uncertain clinical significance were associated with higher and slightly higher rates of management recommendations, respectively, compared with benign/no variants (RR=1.34; 95% CI (1.22–1.47); RR=1.23; 95% CI (1.09–1.38)). Recommendation rates for clinically significant versus uncertain results depended upon how uncertainty was classified (RR_broad=1.09; 95% CI (0.99–1.2); RR_narrow=1.12; 95% CI (1.02–1.24)). Recommendation rates also varied by the child's age and provider type. In conclusion, medical recommendations follow CMA for the majority of children. Compared with benign CMA results, clinically significant CMA variants are a significant driver of pediatric medical recommendations. Variants of uncertain clinical significance drive recommendations, but to a lesser extent. As a broadening range of specialists will need to respond to CMA results, targeted capacity building is warranted.

Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers

Although the field of pharmacogenetics has existed for decades, practioners have been slow to implement pharmacogenetic testing in clinical care. Numerous publications describe the barriers to clinical implementation of pharmacogenetics. Recently, several freely available resources have been developed to help address these barriers. In this review, we discuss current programs that use preemptive genotyping to optimize the pharmacotherapy of patients. Array-based preemptive testing includes a large number of relevant pharmacogenes that impact multiple high-risk drugs. Using a preemptive approach allows genotyping results to be available prior to any prescribing decision so that genomic variation may be considered as an inherent patient characteristic in the planning of therapy. This review describes the common elements among programs that have implemented preemptive genotyping and highlights key processes for implementation, including clinical decision support.

Pharmacogenetic approaches in the treatment of alcohol use disorders: addressing clinical utility and implementation thresholds

Despite advances in characterizing genetic influences on addiction liability and treatment response, clinical applications of these efforts have been slow to evolve. Although challenges to clinical translation remain, stakeholders already face decisions about evidentiary thresholds for the uptake of pharmacogenetic tests in practice. There is optimism about potential pharmacogenetic applications for the treatment of alcohol use disorders, with particular interest in the OPRM1 A118G polymorphism as a moderator of naltrexone response. Findings from human and animal studies suggest preliminary evidence for the clinical validity of this association; on this basis, arguments for clinical implementation can be made in accordance with existing frameworks for the uptake of genomic applications. However, generating evidence-based guidelines requires evaluating the clinical utility of pharmacogenetic tests. This goal will remain challenging, largely due to minimal data to inform clinical utility estimates. The pace of genomic discovery highlights the need for clinical utility and implementation research to inform future translation efforts. Near-term implementation of promising pharmacogenetic tests can help expedite this goal, generating an evidence base to enable efficient translation as additional gene-drug associations are discovered.

The Ethical, Legal, and Social Implications Program of the National Human Genome Research Institute: reflections on an ongoing experiment

For more than 20 years, the Ethical, Legal, and Social Implications (ELSI) Program of the National Human Genome Research Institute has supported empirical and conceptual research to anticipate and address the ethical, legal, and social implications of genomics. As a component of the agency that funds much of the underlying science, the program has always been an experiment. The ever-expanding number of issues the program addresses and the relatively low level of commitment on the part of other funding agencies to support such research make setting priorities especially challenging. Program-supported studies have had a significant impact on the conduct of genomics research, the implementation of genomic medicine, and broader public policies. The program's influence is likely to grow as ELSI research, genomics research, and policy development activities become increasingly integrated. Achieving the benefits of increased integration while preserving the autonomy, objectivity, and intellectual independence of ELSI investigators presents ongoing challenges and new opportunities.

Prioritizing genomic applications for action by level of evidence: a horizon-scanning method

As evidence accumulates on the use of genomic tests and other health-related applications of genomic technologies, decision makers may increasingly seek support in identifying which applications have sufficiently robust evidence to suggest they might be considered for action. As an interim working process to provide such support, we developed a horizon-scanning method that assigns genomic applications to tiers defined by availability of synthesized evidence. We illustrate an application of the method to pharmacogenomics tests.

Pharmacogenetics in clinical practice: how far have we come and where are we going?

Recent years have seen great advances in our understanding of genetic contributors to drug response. Drug discovery and development around targeted genetic (somatic) mutations has led to a number of new drugs with genetic indications, particularly for the treatment of cancers. Our knowledge of genetic contributors to variable drug response for existing drugs has also expanded dramatically, such that the evidence now supports clinical use of genetic data to guide treatment in some situations, and across a variety of therapeutic areas. Clinical implementation of pharmacogenetics has seen substantial growth in recent years and groups are working to identify the barriers and best practices for pharmacogenetic-guided treatment. The advances and challenges in these areas are described and predictions about future use of genetics in drug therapy are discussed.

PG4KDS: a model for the clinical implementation of pre-emptive pharmacogenetics

Pharmacogenetics is frequently cited as an area for initial focus of the clinical implementation of genomics. Through the PG4KDS protocol, St. Jude Children's Research Hospital pre-emptively genotypes patients for 230 genes using the Affymetrix Drug Metabolizing Enzymes and Transporters (DMET) Plus array supplemented with a CYP2D6 copy number assay. The PG4KDS protocol provides a rational, stepwise process for implementing gene/drug pairs, organizing data, and obtaining consent from patients and families. Through August 2013, 1,559 patients have been enrolled, and four gene tests have been released into the electronic health record (EHR) for clinical implementation: TPMT, CYP2D6, SLCO1B1, and CYP2C19. These genes are coupled to 12 high-risk drugs. Of the 1,016 patients with genotype test results available, 78% of them had at least one high-risk (i.e., actionable) genotype result placed in their EHR. Each diplotype result released to the EHR is coupled with an interpretive consult that is created in a concise, standardized format. To support-gene based prescribing at the point of care, 55 interruptive clinical decision support (CDS) alerts were developed. Patients are informed of their genotyping result and its relevance to their medication use through a letter. Key elements necessary for our successful implementation have included strong institutional support, a knowledgeable clinical laboratory, a process to manage any incidental findings, a strategy to educate clinicians and patients, a process to return results, and extensive use of informatics, especially CDS. Our approach to pre-emptive clinical pharmacogenetics has proven feasible, clinically useful, and scalable.

Comparison of delivery strategies for pharmacogenetic testing services

The number and use of pharmacogenetic tests to assess a patient's likelihood of response or risk of an adverse event is expanding across medical specialties and becoming more prevalent. During this period of development and translation, different approaches are being investigated to optimize delivery of pharmacogenetic services. In this paper, we review pre-emptive and point-of-care delivery approaches currently implemented or being investigated and discuss the advantages and disadvantages of each approach. The continued growth in knowledge about the genetic basis of drug response combined with development of new and less expensive testing technologies and electronic medical records will impact future delivery systems. Regardless of delivery approach, the currently limited knowledge of health professionals about genetics generally or PGx specifically will remain a major obstacle to utilization.

Increasing value and reducing waste in research design, conduct, and analysis

Correctable weaknesses in the design, conduct, and analysis of biomedical and public health research studies can produce misleading results and waste valuable resources. Small effects can be difficult to distinguish from bias introduced by study design and analyses. An absence of detailed written protocols and poor documentation of research is common. Information obtained might not be useful or important, and statistical precision or power is often too low or used in a misleading way. Insufficient consideration might be given to both previous and continuing studies. Arbitrary choice of analyses and an overemphasis on random extremes might affect the reported findings. Several problems relate to the research workforce, including failure to involve experienced statisticians and methodologists, failure to train clinical researchers and laboratory scientists in research methods and design, and the involvement of stakeholders with conflicts of interest. Inadequate emphasis is placed on recording of research decisions and on reproducibility of research. Finally, reward systems incentivise quantity more than quality, and novelty more than reliability. We propose potential solutions for these problems, including improvements in protocols and documentation, consideration of evidence from studies in progress, standardisation of research efforts, optimisation and training of an experienced and non-conflicted scientific workforce, and reconsideration of scientific reward systems.

Using systems approaches to address challenges for clinical implementation of pharmacogenomics

Many genetic variants have been shown to affect drug response through changes in drug efficacy and likelihood of adverse effects. Much of pharmacogenomic science has focused on discovering and clinically implementing single gene variants with large effect sizes. Given the increasing complexities of drug responses and their variability, a systems approach may be enabling for discovery of new biology in this area. Further, systems approaches may be useful in addressing challenges in moving these data to clinical implementation, including creation of predictive models of drug response phenotypes, improved clinical decision-making through complex biological models, improving strategies for integrating genomics into clinical practice, and evaluating the impact of implementation programs on public health.

Easing the global burden of diarrhoeal disease: can synthetic biology help?

The Millennium Declaration committed the 193 member states of the United Nations to end poverty by 2015. Despite the efforts of the UN and World Health Organisation, and the G8 commitment to spend a fixed proportion of gross national income on overseas aid, more than 2.6 billion people still lack access to proper sanitation. The absence of effective public health strategies in developing countries results in significant health burdens following gastrointestinal infections. Diarrhoea associated with infections resulting from oral-faecal contamination is the second leading cause of death in children under 5 years of age, primarily in Africa and South Asia. Currently there are no appropriate vaccines that could be easily administered on a global scale to prevent these infections. Synthetic biology has the potential to contribute to development of such vaccines. Our work is directed at developing a range of multivalent oral vaccines against the most common diarrhoea-causing bacteria, e.g., Escherichia coli, Shigella and Salmonella. If synthetic biology is to avoid the suspicion and possible revulsion of the public, scientists need to demonstrate that this new field has something real to offer.

Personalizing health care: feasibility and future implications

Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.

Are health technology assessments of pharmacogenetic tests feasible? A case study of CYP2D6 testing in the treatment of breast cancer with tamoxifen

This paper reports the process and experience of the design and conduct of a UK-based health technology assessment (HTA) of CYP2D6 pharmacogenetic testing to inform the targeted use of tamoxifen for the treatment of breast cancer. Examples of particular challenges for conducting a HTA are highlighted. It is clear from the HTA process described here that a common finding of similar future HTAs will have gaps in the evidence base, particularly in relation to evidence to inform cost–effectiveness. The lack of evidence is likely to be sufficiently large to result in extreme uncertainty and possibly decisions not to recommend a pharmacogenetic test for use in clinical practice. This has clear negative implications, which may hamper moving pharmacogenetic tests from the research environment into clinical practice and requires attention from both manufacturers of pharmacogenetic tests and key decision-makers responsible for market authorization and reimbursement.

Family history: impact on coronary heart disease risk assessment beyond guideline-defined factors

OBJECTIVE

Family history (FH) provides insights into the effects of shared genomic susceptibilities, environments and behaviors, making it a potentially valuable risk assessment tool for chronic diseases. We assessed whether coronary heart disease (CHD) risk assessment is improved when FH information is added to other clinical information recommended in guidelines.

METHODS

We applied logistic regression analyses to cross-sectional data originally obtained from a UK study of women who delivered a live-born infant between 1951 and 1970. We developed 3 models: Model 1 included only the covariates in a guideline applicable to the population, Model 2 added FH to Model 1, and Model 3 included a fuller range of risk factors. For each model, its ability to discriminate between study subjects with and those without CHD was evaluated and its impact on risk classification examined using the net reclassification index.

RESULTS

FH was an independent risk factor for CHD (odds ratio = 1.7, 95% confidence interval = 1.26-2.47) and improved discrimination beyond guideline-defined clinical factors (p < 0.0006). However, the difference in the area under the curve of 2.8% and the extent of patient reclassification resulting from the inclusion of FH were small (p = 0.11).

CONCLUSION

While FH were a significant independent risk factor for CHD, it added little to risk factors typically included in guidelines.

The development and validation of a decision-analytic model representing the full disease course of acute myeloid leukemia

BACKGROUND

The treatment of acute myeloid leukemia (AML) is moving towards personalized medicine. However, due to the low incidence of AML, it is not always feasible to evaluate the cost-effectiveness of personalized medicine using clinical trials. Decision analytic models provide an alternative data source.

OBJECTIVE

The aim of this study was to develop and validate a decision analytic model that represents the full disease course of AML.

METHODS

We used a micro simulation with discrete event components to incorporate both patient and disease heterogeneity. Input parameters were calculated from patient-level data. Two hematologists critically evaluated the model to ensure face validity. Internal and external validity was tested by comparing complete remission (CR) rates and survival outcomes of the model with original data, other clinical trials and a population-based study.

RESULTS

No significant differences in patient and treatment characteristics, CR rate, 5-year overall and disease-free survival were found between the simulated and original data. External validation showed no significant differences in survival between simulated data and other clinical trials. However, differences existed between the simulated data and a population-based study.

CONCLUSIONS

The model developed in this study is proved to be valid for analysis of an AML population participating in a clinical trial. The generalizability of the model to a broader patient population has not been proven yet. Further research is needed to identify differences between the clinical trial population and other AML patients and to incorporate these differences in the model.