Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants

Enriching iPSC research diversity: Harnessing human biobank collections for improved ethnic representation

Biobanks of human biosamples and cell lines are indispensable for biomedical research on human health and disease and for drug development projects. Many human cell line biobanks worldwide hold collections of lymphoblastoid cell lines (LCLs), representing thousands of affected and control donors from diverse ethnic/ancestry groups. In recent years, induced human pluripotent stem cells (iPSCs) and differentiated human cells derived from these iPSCs have become indispensable for applied biomedical research. Establishing iPSCs remains a laborious and costly step towards generating differentiated human cells. To address this research need, several non-profit and commercial biobanks have established iPSC collections for distribution to researchers, thereby serving as a resource for generating differentiated human cells. The most common starting materials for generation of iPSCs are a skin biopsy for harvesting fibroblasts, or a blood sample for collection of peripheral blood mononuclear cells. However untapped resources include the large established collections of biobanked human LCLs which can be reprogrammed to iPSCs using a variety of published protocols including the use of non-integrating episomal vectors. Many biobanks curate LCLs from diverse ethnic/ancestry populations, an aspect largely absent in most established iPSC biobanks which tend to primarily reflect populations from developed countries. Here, we call upon researchers across the breadth of iPSC research to tap the unique resource of existing and diverse human LCL collections for establishing biobanked iPSC panels that better represent the varied human ethnic (and hence genomic) diversity, thereby benefiting precision medicine and drug development research on a global scale.

Overcoming Barriers to Discovery and Implementation of Equitable Pharmacogenomic Testing in Oncology

Pharmacogenomics (PGx), the study of inherited genomic variation and drug response or safety, is a vital tool in precision medicine. In oncology, testing to identify PGx variants offers patients the opportunity for customized treatments that can minimize adverse effects and maximize the therapeutic benefits of drugs used for cancer treatment and supportive care. Because individuals of shared ancestry share specific genetic variants, PGx factors may contribute to outcome disparities across racial and ethnic categories when genetic ancestry is not taken into account or mischaracterized in PGx research, discovery, and application. Here, we examine how the current scientific understanding of the role of PGx in differential oncology safety and outcomes may be biased toward a greater understanding and more complete clinical implementation of PGx for individuals of European descent compared with other genetic ancestry groups. We discuss the implications of this bias for PGx discovery, access to care, drug labeling, and patient and provider understanding and use of PGx approaches. Testing for somatic genetic variants is now the standard of care in treatment of many solid tumors, but the integration of PGx into oncology care is still lacking despite demonstrated actionable findings from PGx testing, reduction in avoidable toxicity and death, and return on investment from testing. As the field of oncology is poised to expand and integrate germline genetic variant testing, it is vital that PGx discovery and application are equitable for all populations. Recommendations are introduced to address barriers to facilitate effective and equitable PGx application in cancer care.

Star allele search: a pharmacogenetic annotation database and user-friendly search tool of publicly available 1000 Genomes Project biospecimens

Here we describe a new public pharmacogenetic (PGx) annotation database of a large (n = 3,202) and diverse biospecimen collection of 1000 Genomes Project cell lines and DNAs. The database is searchable with a user friendly, web-based tool ( www.coriell.org/StarAllele/Search ). This resource leverages existing whole genome sequencing data and PharmVar annotations to characterize *alleles for each biospecimen in the collection. This new tool is designed to facilitate in vitro functional characterization of *allele haplotypes and diplotypes as well as support clinical PGx assay development, validation, and implementation.

Towards more accurate pharmacogenomic variant effect predictions

Tweetable abstract Accurate variant interpretation has become a key bottleneck for the translation of an individual's pharmacogenome into actionable recommendations. We recommend an integrated use of multiplexed assays, structure-based predictions and biobank data to develop more accurate effect predictors.

Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

Patient Perceptions and Potential Utility of Pharmacogenetic Testing in Chronic Pain Management and Opioid Use Disorder in the Camden Opioid Research Initiative

Pharmacogenetics (PGx) has the potential to improve opioid medication management. Here, we present patient perception data, pharmacogenetic data and medication management trends in patients with chronic pain (arm 1) and opioid use disorder (arm 2) treated at Cooper University Health Care in Camden City, NJ. Our results demonstrate that the majority of patients in both arms of the study (55% and 65%, respectively) are open to pharmacogenetic testing, and most (66% and 69%, respectively) believe that genetic testing has the potential to improve their medical care. Our results further support the potential for CYP2D6 PGx testing to inform chronic pain medication management for poor metabolizers (PMs) and ultrarapid metabolizers (UMs). Future efforts to implement PGx testing in chronic pain management, however, must address patient concerns about genetic test result access and genetic discrimination.