Biology subjects

Gifford, A.

Publications and source records attributed to Gifford, A..

3 recordsLinked to original sources

bioRxiv2022

A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer

Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers. We combined immunohistochemistry (IHC) and molecular profiling to develop an alternative, expression-based signature associated with CD8+ T-cell infiltration of the TIME in high-risk paediatric tumours. Using this novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME.

cancer biology↗

bioRxiv2019

A Phenome-wide Mendelian Randomisation study on genetically determined serum urate levels in UK Biobank cohort

IntroductionThe role of serum urate level has been extensively investigated in observational studies. However, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance.\n\nObjectivesTo explore any causal or pleiotropic association between serum urate level and a broad spectrum of disease outcomes.\n\nMethodsPhenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic models (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 UK Biobank participants. Mendelian Randomisation (MR) analyses were performed to replicate significant findings using various GWAS consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for serum urate.\n\nResultsPheWAS analysis, examining the association with 1,431 disease outcomes, identified a multitude of disease outcomes including gout, hypertension, hypercholesterolemia, and heart diseases (e.g., coronary atherosclerosis, myocardial infarction, and ischaemic heart disease) that were associated (p<3.35e-04) with genetically determined serum urate levels. TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases and blood lipid levels, but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with cardiovascular diseases.\n\nConclusionsHigh serum urate levels are associated with different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a more distal mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.

epidemiology↗

bioRxiv2018

Developing and Evaluating Mappings of ICD-10 and ICD-10-CM codes to Phecodes

BackgroundThe PheCode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) in the electronic health record (EHR).\n\nObjectiveHere, we present our work on the development and evaluation of maps from ICD-10 and ICD-10-CM codes to PheCodes.\n\nMethodsWe mapped ICD-10 and ICD-10-CM codes to PheCodes using a number of methods and resources, such as concept relationships and explicit mappings from the Unified Medical Language System (UMLS), Observational Health Data Sciences and Informatics (OHDSI), Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), and National Library of Medicine (NLM). We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM[->]PheCode map by investigating phenotype reproducibility and conducting a PheWAS.\n\nResultsWe mapped >75% of ICD-10-CM and ICD-10 codes to PheCodes. Of the unique codes observed in the VUMC (ICD-10-CM) and UKBB (ICD-10) cohorts, >90% were mapped to PheCodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease. A PheWAS with a lipoprotein(a) (LPA) genetic variant, rs10455872, using the ICD-9-CM and ICD-10-CM maps replicated two genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P < .001, OR = 1.60 vs. ICD-10-CM: P < .001, OR = 1.60) and with chronic ischemic heart disease (ICD-9-CM: P < .001, OR = 1.5 vs. ICD-10-CM: P < .001, OR = 1.47).\n\nConclusionsThis study introduces the initial \"beta\" versions of ICD-10 and ICD-10-CM to PheCode maps that will enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for high-throughput PheWAS in the EHR.

bioinformatics↗