Moyer, Ann M.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) TPMT/NUDT15 Pharmacogene Curation Expert Panel (PCEP) conducted a comprehensive review of clinical, laboratory, and computational evidence to determine the clinical function assignments for TPMT and NUDT15 star alleles. These genes are critical for the metabolism of thiopurines, which are widely used in the treatment of cancer and autoimmune disorders. Standardized allele function assignment is essential for predicting metabolizer phenotypes and pharmacogenetics‐guided thiopurine dosing. The work presented here includes the first designation of decreased function alleles for both TPMT and NUDT15 , reflecting new clinical data that demonstrate partial loss of enzymatic activity and reduced dose tolerance. The panel also reclassified several alleles previously assigned uncertain or unknown function. The functional assignments were informed by a standardized framework incorporating clinical data, such as thiopurine tolerance and toxicity, as well as in vitro protein activity, ex vivo enzymatic measurements, and in silico variant effect prediction tools. These updates enhance the precision of genotype‐to‐phenotype mapping and support more personalized thiopurine therapy across diverse patient populations.

Complement-mediated hemolytic uremic syndrome (CM-HUS), commonly referred to as atypical HUS, is a rare thrombotic microangiopathy caused by uncontrolled activation of the alternative complement pathway, typically triggered by a “two-hit” mechanism. It is characterized by microangiopathic hemolytic anemia, thrombocytopenia, and end-organ damage, most commonly affecting the kidneys. While our understanding of the complement system has advanced significantly, CM-HUS remains a complex, heterogeneous disorder influenced by a spectrum of genetic variants, risk haplotypes, and acquired factors such as anti-factor H autoantibodies. This review highlights the current knowledge of CM-HUS pathogenesis, focusing on genetic variants in regulatory and activating proteins of the complement system. We also discuss the diagnostic complexity posed by incomplete penetrance, overlapping phenotypes, and limitations of genetic and functional assays. Emerging ex-vivo assays and complement biomarkers are explored as tools for refining diagnosis and risk stratification. The use of complement inhibitors such as eculizumab and ravulizumab has significantly improved renal outcomes and survival. This review provides a comprehensive, clinically grounded update on the genetics, pathophysiology, diagnostics, and therapeutic considerations in CM-HUS, aiming to provide clinicians and researchers with a deeper understanding of this complex, complement-driven disease.

Abstract Background: Germline predisposition states (GPS), including inherited bone marrow failure syndromes (IBMFS) are seen in 5-10% of individuals with myeloid neoplasms (MN). Given overlap with somatic driver genes, blood, saliva, and buccal swabs are suboptimal specimens for GPS testing in MN; the latter two due to contamination with blood cells(Jerez, 2024). Skin biopsy with fibroblast culture-derived DNA remains the gold standard, but the process is expensive, takes 6-8 weeks to complete, and fibroblast culture failure can occur in some patients with telomere biology disorders. Given the need to rapidly screen donors for HSCT and adjust therapy/conditioning in certain IBMFS, we developed a rapid hematologic germline testing algorithm using hair follicle-derived DNA. Methods The study was conducted after IRB approval through the Mayo Clinic GPS/IBMFS clinic. To perform PCR-based hair follicle testing, we obtained 15-25 hair follicles (bulbs) per patient. The bulbs were placed in a microcentrifuge tube and subjected to a 24-hour digestion and extraction protocol using a modified version of the QIAGEN DNA Purification from Dried Blood Spots protocol. DNA quality was assessed by Nanodrop and Qubit.Testing included either validation of known or suspected germline variants (e.g., RUNX1, DDX41, ETV6), or panel testing (e.g., GPS or IBMFS). Pathogenic variants were validated by repeat testing in a CAP/CLIA certified lab prior to medical decision making. 23 patients underwent whole exome sequencing (WES) in a CAP/CLIA lab, and 13 patients had concurrent germline testing using alternate DNA sources (8 skin biopsies, 1 saliva, 4 buccal swabs). Results Ninety-three patients (49 male, 53%) underwent hair follicle-derived germline DNA testing (median age 63 years). Indications for germline testing included the presence of a suspected variant on somatic testing with variant allele frequency (VAF) between 35%-60% ( n= 66, 71%), patient age <50 years (n=26, 28%), antecedent thrombocytopenia (n=15, 16%), presence of syndromic features (n=4, 4%), patient history of multiple malignancies (n=4, 4%), and positive family history (n=15, 16%). Time to reporting variants after receipt of hair follicle-derived DNA was <14 days. There were zero complications associated with the procedure. DNA quality as assessed by Nanodrop revealed a median nucleic acid concentration of 25.64 ng/μL. The median A260/A280 ratio was 1.86 (range 0.701-2.133) and the median A260/A230 ratio was 1.27 (range -8.193-9.223). DNA quality was insufficient for chromosomal microarray (CMA) assessments. Of 93 patients evaluated, 67 (72%) had ≥1 germline variant, with 3 having >1 germline variant for a total of 72 variants. Pathogenic/likely pathogenic variants identified included: DDX41 (30, 41.7%), TP53 (4, 5.6%), ANKRD26 (4, 5.6%), RUNX1 (4, 5.6%), GATA2 (3, 4.2%), TERT (3, 4.2%), JAK3 (2, 2.8%), SAMD9/SAMD9L (2, 2.8%), ATM (2, 2.8%), and 1 each (1.4%) in BCL2, BCOR, BPGM, CDKN2A, CEBPA, CHEK2, CSF3R, CXCR4, DNMT3A, ETV6, GATA1, MPL, MPO, POT1, PTPN11, RPL5, and SH2B3. In the 13 patients that had concurrent germline testing, results were identical in all cases, except for 1 false negative in a buccal specimen (PTPN11) and 1 case of germline mosaicism, where the TP53 variant was identified in the blood and hair follicle DNA but not in skin fibroblast DNA. After testing, 30 (44.7%) patients had a change in their management plan: 8 (27%) with DDX41-GPS were observed due to known latency of DDX41-mutant MN, 6 (20%) were referred for HSCT based on the germline variant (CEBPA, DDX41, GATA1, GATA2, RPL5, TP53), 6 (20%) were placed on an enhanced surveillance plan for cancer screening (1 ANKRD26, 3 TERT, 2 TP53), 4 (13%) patients undergoing HSCT had related donors removed from consideration as they were carriers (2 DDX41, 1 ANKRD26, and 1 SAMD9), 2 (7%) patients with neutropenia and underlying germline variants (1 ATM, 1 DDX41) had G-CSF therapy discontinued due to concerns for clonal stimulation, and 1 (3%) patient with a germline CDKN2A mutation (hematological diagnosis- CMML) was recommended to avoid radiation therapy for prostate cancer. Conclusion In summary, we validate a rapid and simple hair follicle-derived DNA based germline assessment for patients with hematological disorders and demonstrate a positive impact on clinical care in 44% of patients. We are currently optimizing DNA quality so that germline copy number alterations can be better detected using CMA.

Context.— Clinical laboratories are increasingly implementing pharmacogenomic (PGx) testing. Although PGx is similar to genetic testing for other indications, there are unique aspects that laboratories should consider. Objective.— To aid clinical laboratories that are implementing clinical PGx testing by describing characteristics of PGx test design and validation, as well as approaches to reporting. Resources that are useful for clinical laboratories performing PGx testing will be highlighted. Design.— The College of American Pathologists formed a workgroup composed of laboratorians with expertise in clinical PGx testing. The workgroup included representatives from the Association for Molecular Pathology and the American College of Medical Genetics and Genomics. The workgroup reviewed pertinent literature, as well as experience from proficiency testing and from members’ laboratories. Results.— The workgroup recommends that laboratories implementing PGx consider the following concepts: testing platform, test design (ie, selection of pharmacogenes and variants/alleles), use of reference materials during test development and as controls during clinical runs, star allele and standard nomenclature systems, translations from genotype to predicted phenotype, and considerations for result reporting including making medication recommendations. The workgroup provides considerations when using report vendors, emphasizing the clinical laboratory’s role and responsibility when implementing such reporting tools from vendors. Conclusions.— Clinical laboratories should be familiar with the fundamentals of PGx, ensure that PGx testing meets the applicable regulatory requirements for all aspects of the clinical laboratory testing process, and follow recommendations for standardization of nomenclature and reporting.

Pharmacogenomics (PGx) has the potential to improve prescribing practices. In 2012, our institution developed a multidisciplinary program to implement PGx-CDS to support the clinical practice. CDS has been implemented for 45 drug-gene interactions, in 42,000 patients with PGx tests results, and in 2023, approximately 9281 CDS interventions have triggered at the time of prescribing. The program has been successful.

ABSTRACT Major depressive disorder (MDD) is a substantial public health challenge. Pharmacogenomics (PGx), which identifies genetic variations that predict drug treatment outcomes, may have utility for clinical practice, but adequate representation of all populations is needed. As precision medicine in psychiatry moves towards the use of Artificial Intelligence (AI) and Machine Learning (ML) to predict treatment outcomes using PGx data, representation of diverse populations will be especially important in order to mitigate algorithmic bias and achieve equitable and generalizable findings. This work sought to quantify population diversity in pharmacogenomic studies of MDD through a systematic review. Data from 390 MDD antidepressant PGx studies were extracted from 5739 articles screened. Studies summarized were predominantly conducted in Europe, East Asia, and North America. Across all global studies, the study population comprised 57.3% White, 36.4% Asian, 1.7% Black, 3.5% Hispanic/Latino, and 0.1% Native American or Indigenous participants. Only sixty‐three (16.2%) studies included Black or Latino/Hispanic patients. Additionally, Black, Asian, Hispanic/Latino, and American Indian/Alaska Native populations were statistically underrepresented in U.S. study populations when compared to national census data, while Asian and Black populations were underrepresented in the United Kingdom. The overrepresentation of participants from a limited number of countries combined with the underrepresentation of Black and Hispanic/Latino populations could impact the extent to which pharmacogenomic testing and associated AI/ML‐based PGx tools could individualize antidepressant medication regimens for treating MDD.

Barriers to incorporating pharmacogenetics into routine clinical practice in the United States are well documented. Initial surveys by the Clinical Pharmacogenetics Implementation Consortium (CPIC) in 2009 and 2010 identified barriers across four key domains that have hindered the widespread adoption of clinical pharmacogenetic testing. These are presented verbatim as: (i) absence of a definition of the processes required to interpret genotype information and to translate genetic information into clinical actions; (ii) need for recommended drug/gene pairs to implement clinically now; (iii) clinician resistance to consider pharmacogenetic information at the bedside; and (iv) concerns about test costs and reimbursement. Over time, many of these challenges have been overcome, and clinical pharmacogenetic testing has subsequently reached broader implementation. Despite this progress, several barriers remain that block further adoption. This narrative review used authors' expertise and experience to identify and describe current barriers to pharmacogenetic implementation across seven domains in the United States: equity and inclusion; guidelines and supporting evidence; regulatory agency oversight; payer coverage and insurance; availability of quality pharmacogenetic tests; electronic health records; and provider and patient education. Within each domain, it revisits past successes and challenges and explores remaining barriers. We also propose solutions to address ongoing challenges across these domains, including further expansion of recommendations beyond pharmacogenetic‐specific guidelines, standards for designing clinical decision support tools, and broader pharmacogenetics education. Addressing these remaining obstacles directs work to enable broader adoption of clinical pharmacogenetic implementation to ultimately improve patient outcomes.

ABSTRACTPharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary aim of this systematic review was to summarize antidepressant pharmacogenetic studies to enhance understanding of the genes, variants, datatypes/methodologies, and outcomes investigated in the context of MDD. The secondary aim was to identify clinical genetic panels indicated for antidepressant prescribing and summarize their genes and variants. Screening of N = 5793 articles yielded N = 390 for inclusion, largely comprising adult (≥ 18 years) populations. Top‐studied variants identified in the search were discussed and compared with those represented on the N = 34 clinical genetic panels that were identified. Summarization of articles revealed sources of heterogeneity across studies and low rates of replicability of pharmacogenetic associations. Heterogeneity was present in outcome definitions, treatment regimens, and differential inclusion of mediating variables in analyses. Efficacy outcomes (i.e., response, remission) were studied at greater frequency than adverse‐event outcomes. Studies that used advanced analytical approaches, such as machine learning, to integrate variants with complimentary biological datatypes were fewer in number but achieved higher rates of significant associations with treatment outcomes than candidate variant approaches. As large biological datasets become more prevalent, machine learning will be an increasingly valuable tool for parsing the complexity of antidepressant response. This review provides valuable context and considerations surrounding pharmacogenetic associations in MDD which will help inform future research and translation efforts for guiding antidepressant care.