Pharmacogenetic and clinical risk factors for bevacizumab-related gastrointestinal hemorrhage in prostate cancer patients treated on CALGB 90401 (Alliance).

The objective of this study was to discover clinical and pharmacogenetic factors associated with bevacizumab-related gastrointestinal hemorrhage in Cancer and Leukemia Group B (Alliance) 90401. Patients with metastatic castration-resistant prostate cancer received docetaxel and prednisone ± bevacizumab. Patients were genotyped using Illumina HumanHap610-Quad and assessed using cause-specific risk for association between single nucleotide polymorphisms (SNPs) and gastrointestinal hemorrhage. In 1008 patients, grade 2 or higher gastrointestinal hemorrhage occurred in 9.5% and 3.8% of bevacizumab (n = 503) and placebo (n = 505) treated patients, respectively. Bevacizumab (P < 0.001) and age (P = 0.002) were associated with gastrointestinal hemorrhage. In 616 genetically estimated Europeans (n = 314 bevacizumab and n = 302 placebo treated patients), grade 2 or higher gastrointestinal hemorrhage occurred in 9.6% and 2.0% of patients, respectively. One SNP (rs1478947; HR 6.26; 95% CI 3.19-12.28; P = 9.40 × 10-8) surpassed Bonferroni-corrected significance. Grade 2 or higher gastrointestinal hemorrhage rate was 33.3% and 6.2% in bevacizumab-treated patients with the AA/AG and GG genotypes, versus 2.9% and 1.9% in the placebo arm, respectively. Prospective validation of these findings and functional analyses are needed to better understand the genetic contribution to treatment-related gastrointestinal hemorrhage.

A test of automated use of electronic health records to aid in diagnosis of genetic disease.

PURPOSE: Automated use of electronic health records may aid in decreasing the diagnostic delay for rare diseases. The phenotype risk score (PheRS) is a weighted aggregate of syndromically related phenotypes that measures the similarity between an individual's conditions and features of a disease. For some diseases, there are individuals without a diagnosis of that disease who have scores similar to diagnosed patients. These individuals may have that disease but not yet be diagnosed. METHODS: We calculated the PheRS for cystic fibrosis (CF) for 965,626 subjects in the Vanderbilt University Medical Center electronic health record. RESULTS: Of the 400 subjects with the highest PheRS for CF, 248 (62%) had been diagnosed with CF. Twenty-six of the remaining participants, those who were alive and had DNA available in the linked DNA biobank, underwent clinical review and sequencing analysis of CFTR and SERPINA1. This uncovered a potential diagnosis for 2 subjects, 1 with CF and 1 with alpha-1-antitrypsin deficiency. An additional 7 subjects had pathogenic or likely pathogenic variants, 2 in CFTR and 5 in SERPINA1. CONCLUSION: These findings may be clinically actionable for the providers caring for these patients. Importantly, this study highlights feasibility and challenges for future implications of this approach.

Population genetic testing and SERPINA1 sequencing identifies unidentified alpha-1 antitrypsin deficiency alleles and gene-environment interaction with hepatitis C infection.

Alpha-1 antitrypsin deficiency (AATD), a relatively common autosomal recessive genetic disorder, is underdiagnosed in symptomatic individuals. We sought to compare the risk of liver transplantation associated with hepatitis C infection with AATD heterozygotes and homozygotes and determine if SERPINA1 sequencing would identify undiagnosed AATD. We performed a retrospective cohort study in a deidentified Electronic Health Record (EHR)-linked DNA biobank with 72,027 individuals genotyped for the M, Z, and S alleles in SERPINA1. We investigated liver transplantation frequency by genotype group and compared with hepatitis C infection. We performed SERPINA1 sequencing in carriers of pathogenic AATD alleles who underwent liver transplantation. Liver transplantation was associated with the Z allele (ZZ: odds ratio [OR] = 1.31, p<2e-16; MZ: OR = 1.02, p = 1.2e-13) and with hepatitis C (OR = 1.20, p<2e-16). For liver transplantation, there was a significant interaction between genotype and hepatitis C (ZZ: interaction OR = 1.23, p = 4.7e-4; MZ: interaction OR = 1.11, p = 6.9e-13). Sequencing uncovered a second, rare, pathogenic SERPINA1 variant in six of 133 individuals with liver transplants and without hepatitis C. Liver transplantation was more common in individuals with AATD risk alleles (including heterozygotes), and AATD and hepatitis C demonstrated evidence of a gene-environment interaction in relation to liver transplantation. The current AATD screening strategy may miss diagnoses whereas SERPINA1 sequencing may increase diagnostic yield for AATD, stratify risk for liver disease, and inform clinical management for individuals with AATD risk alleles and liver disease risk factors.

Systemic inhibition of PTPN22 augments anticancer immunity.

Both epidemiologic and cellular studies in the context of autoimmune diseases have established that protein tyrosine phosphatase non-receptor type 22 (PTPN22) is a key regulator of T cell receptor (TCR) signaling. However, its mechanism of action in tumors and its translatability as a target for cancer immunotherapy have not been established. Here we show that a germline variant of PTPN22, rs2476601, portended a lower likelihood of cancer in patients. PTPN22 expression was also associated with markers of immune regulation in multiple cancer types. In mice, lack of PTPN22 augmented antitumor activity with greater infiltration and activation of macrophages, natural killer (NK) cells, and T cells. Notably, we generated a novel small molecule inhibitor of PTPN22, named L-1, that phenocopied the antitumor effects seen in genotypic PTPN22 knockout. PTPN22 inhibition promoted activation of CD8+ T cells and macrophage subpopulations toward MHC-II expressing M1-like phenotypes, both of which were necessary for successful antitumor efficacy. Increased PD1-PDL1 axis in the setting of PTPN22 inhibition could be further leveraged with PD1 inhibition to augment antitumor effects. Similarly, cancer patients with the rs2476601 variant responded significantly better to checkpoint inhibitor immunotherapy. Our findings suggest that PTPN22 is a druggable systemic target for cancer immunotherapy.

Genome-Wide Association Study Identifies Genetic Risk Factors for Spastic Cerebral Palsy.

BACKGROUND: Although many clinical risk factors of spastic cerebral palsy (CP) have been identified, the genetic basis of spastic CP is largely unknown. Here, using whole-genome genetic information linked to a deidentified electronic health record (BioVU) with replication in the UK Biobank and FinnGen, we perform the first genome-wide association study (GWAS) for spastic CP. OBJECTIVE: To define the genetic basis of spastic CP. METHODS: Whole-genome data were obtained using the multi-ethnic genotyping array (MEGA) genotyping array capturing single-nucleotide polymorphisms (SNPs), minor allele frequency (MAF) > 0.01, and imputation quality score (r2) > 0.3, imputed based on the 1000 genomes phase 3 reference panel. Threshold for genome-wide significance was defined after Bonferroni correction for the total number of SNPs tested (P < 5.0 × 10-8). Replication analysis (defined as P < .05) was performed in the UK Biobank and FinnGen. RESULTS: We identify 1 SNP (rs78686911) reaching genome-wide significance with spastic CP. Expression quantitative trait loci (eQTL) analysis suggests that rs78686911 decreases expression of GRIK4, a gene that encodes a high-affinity kainate glutamatergic receptor of largely unknown function. Replication analysis in the UK Biobank and FinnGen reveals additional SNPs in the GRIK4 loci associated with CP. CONCLUSION: To our knowledge, we perform the first GWAS of spastic CP. Our study indicates that genetic variation contributes to CP risk.

Prostaglandin I2 signaling licenses Treg suppressive function and prevents pathogenic reprogramming.

Tregs restrain both the innate and adaptive immune systems to maintain homeostasis. Allergic airway inflammation, characterized by a Th2 response that results from a breakdown of tolerance to innocuous environmental antigens, is negatively regulated by Tregs. We previously reported that prostaglandin I2 (PGI2) promoted immune tolerance in models of allergic inflammation; however, the effect of PGI2 on Treg function was not investigated. Tregs from mice deficient in the PGI2 receptor IP (IP KO) had impaired suppressive capabilities during allergic airway inflammatory responses compared with mice in which PGI2 signaling was intact. IP KO Tregs had significantly enhanced expression of immunoglobulin-like transcript 3 (ILT3) compared with WT Tregs, which may contribute to the impairment of the IP KO Treg's ability to suppress Th2 responses. Using fate-mapping mice, we reported that PGI2 signaling prevents Treg reprogramming toward a pathogenic phenotype. PGI2 analogs promoted the differentiation of naive T cells to Tregs in both mice and humans via repression of ß-catenin signaling. Finally, a missense variant in IP in humans was strongly associated with chronic obstructive asthma. Together, these data support that PGI2 signaling licenses Treg suppressive function and that PGI2 is a therapeutic target for enhancing Treg function.

Real-time clinical note monitoring to detect conditions for rapid follow-up: A case study of clinical trial enrollment in drug-induced torsades de pointes and Stevens-Johnson syndrome.

Identifying acute events as they occur is challenging in large hospital systems. Here, we describe an automated method to detect 2 rare adverse drug events (ADEs), drug-induced torsades de pointes and Stevens-Johnson syndrome and toxic epidermal necrolysis, in near real time for participant recruitment into prospective clinical studies. A text processing system searched clinical notes from the electronic health record (EHR) for relevant keywords and alerted study personnel via email of potential patients for chart review or in-person evaluation. Between 2016 and 2018, the automated recruitment system resulted in capture of 138 true cases of drug-induced rare events, improving recall from 43% to 93%. Our focused electronic alert system maintained 2-year enrollment, including across an EHR migration from a bespoke system to Epic. Real-time monitoring of EHR notes may accelerate research for certain conditions less amenable to conventional study recruitment paradigms.

Procurement of shared data instruments for Research Electronic Data Capture (REDCap).

REDCap (Research Electronic Data Capture) is a web-based software solution and tool set that allows biomedical researchers to create secure online forms for data capture, management and analysis with minimal effort and training. The Shared Data Instrument Library (SDIL) is a relatively new component of REDCap that allows sharing of commonly used data collection instruments for immediate study use by research teams. Objectives of the SDIL project include: (1) facilitating reuse of data dictionaries and reducing duplication of effort; (2) promoting the use of validated data collection instruments, data standards and best practices; and (3) promoting research collaboration and data sharing. Instruments submitted to the library are reviewed by a library oversight committee, with rotating membership from multiple institutions, which ensures quality, relevance and legality of shared instruments. The design allows researchers to download the instruments in a consumable electronic format in the REDCap environment. At the time of this writing, the SDIL contains over 128 data collection instruments. Over 2500 instances of instruments have been downloaded by researchers at multiple institutions. In this paper we describe the library platform, provide detail about experience gained during the first 25months of sharing public domain instruments and provide evidence of impact for the SDIL across the REDCap consortium research community. We postulate that the shared library of instruments reduces the burden of adhering to sound data collection principles while promoting best practices.

Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience.

BACKGROUND: Systematic study of clinical phenotypes is important for a better understanding of the genetic basis of human diseases and more effective gene-based disease management. A key aspect in facilitating such studies requires standardized representation of the phenotype data using common data elements (CDEs) and controlled biomedical vocabularies. In this study, the authors analyzed how a limited subset of phenotypic data is amenable to common definition and standardized collection, as well as how their adoption in large-scale epidemiological and genome-wide studies can significantly facilitate cross-study analysis. METHODS: The authors mapped phenotype data dictionaries from five different eMERGE (Electronic Medical Records and Genomics) Network sites studying multiple diseases such as peripheral arterial disease and type 2 diabetes. For mapping, standardized terminological and metadata repository resources, such as the caDSR (Cancer Data Standards Registry and Repository) and SNOMED CT (Systematized Nomenclature of Medicine), were used. The mapping process comprised both lexical (via searching for relevant pre-coordinated concepts and data elements) and semantic (via post-coordination) techniques. Where feasible, new data elements were curated to enhance the coverage during mapping. A web-based application was also developed to uniformly represent and query the mapped data elements from different eMERGE studies. RESULTS: Approximately 60% of the target data elements (95 out of 157) could be mapped using simple lexical analysis techniques on pre-coordinated terms and concepts before any additional curation of terminology and metadata resources was initiated by eMERGE investigators. After curation of 54 new caDSR CDEs and nine new NCI thesaurus concepts and using post-coordination, the authors were able to map the remaining 40% of data elements to caDSR and SNOMED CT. A web-based tool was also implemented to assist in semi-automatic mapping of data elements. CONCLUSION: This study emphasizes the requirement for standardized representation of clinical research data using existing metadata and terminology resources and provides simple techniques and software for data element mapping using experiences from the eMERGE Network.

Is removal of clavicle plate after fracture union necessary?

PURPOSE: To review whether clavicle plates should be removed after union of the fracture. MATERIALS AND METHODS: 48 patients with middle third clavicle fractures treated by plating were assessed with UCLA shoulder rating and Oxford shoulder scores. RESULTS: At an average follow up of 13 months,96% of 27 patients with plates out recommended its removal. 86% of 21 patients with plates in were happy to keep them. CONCLUSIONS: We recommend leaving clavicle plates in unless requested by the patient. LEVEL OF EVIDENCE: IV-retrospective study.

Evaluating Phenotypic Data Elements for Genetics and Epidemiological Research: Experiences from the eMERGE and PhenX Network Projects.

Combining genome-wide association studies (GWAS) data with clinical information from the electronic medical record (EMR) provide unprecedented opportunities to identify genetic variants that influence susceptibility to common, complex diseases. While mining the vastness of EMR greatly expands the potential for conducting GWAS, non-standardized representation and wide variability of clinical data and phenotypes pose a major challenge to data integration and analysis. To address this requirement, we present experiences and methods developed to map phenotypic data elements from eMERGE (Electronic Medical Record and Genomics) to PhenX (Consensus Measures for Phenotypes and Exposures) and NCI's Cancer Data Standards Registry and Repository (caDSR). Our results suggest that adopting multiple standards and biomedical terminologies will expose studies to a broader user community and enhance interoperability with a wider range of studies, in turn promoting cross-study pooling of data to detect both more subtle and more complex genotype-phenotype associations.

Exploratory visual analysis of pharmacogenomic results.

Comprehensive analysis of expansive pharmacogenomic datasets is a daunting challenge. A thorough exploration of experimental results requires both statistical and annotative information. Therefore, appropriate analysis tools must bring a readily-accessible, flexible combination of statistics and biological annotation to the user's desktop. We present the Exploratory Visual Analysis (EVA) software and database as such a tool and demonstrate its utility in replicating the findings of an earlier pharmacogenomic study as well as elucidating novel biologically plausible hypotheses. EVA brings all of the often disparate pieces of analysis together in an infinitely flexible visual display that is amenable to any type of statistical result and biological question. Here, we describe the motivations for developing EVA, detail the database and custom graphical user interface (GUI), provide an example of its application to a publicly available pharmacogenomic dataset, and discuss the broad utility of the EVA tool for the pharmacogenomics community.