5 research outputs found
Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.
Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies
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Quality of Life of Prostate Cancer Survivors Participating in a Remotely Delivered Web-Based Behavioral Intervention Pilot Randomized Trial
BackgroundFollowing a prostate cancer (PC) diagnosis, treatment-related symptoms may result in diminished quality of life (QoL). Improved diet and increased exercise may improve QoL in men with PC.MethodsWe conducted a 4-arm pilot randomized trial to assess feasibility and acceptability of a 3-month web-based diet and exercise intervention, among men (>18 years of age) with PC (reported elsewhere). The purpose of this study is to describe the change in QoL measured by surveys (eg, QLQ-C30, PROMIS Fatigue) at enrollment and following the intervention. Men were randomized 1:1:1:1 to increasing levels of web-based behavioral support: Level 1: website; Level 2: Level 1 plus personalized diet and exercise prescription; Level 3: Levels 1-2 plus Fitbit and text messages; Level 4: Levels 1-3 plus 2 30-minute coaching calls. T-tests were used to compare pre-post change in mean QoL scores between each Level and Level 1.ResultsTwo hundred and two men consented and were randomized (n = 49, 51, 50, 52 for Levels 1-4, respectively). Men were predominantly white (93%), with a median age of 70 years (Intra-quartile Range [IQR]: 65,75) and 3 years (IQR: 1,9) post primary treatment for mostly localized disease (74% with T1-2). There were no meaningful changes in QoL, but there were notable trends. Level 3 participants had small improvements in QLQ-C30 Global Health (5.46; 95% CI: -0.02, 10.95) compared to Level 1. In contrast, Level 2 participants trended toward decreasing Global QoL (-2.31, 95% CI: -8.05, 3.42), which may reflect declines in function (eg, Cognitive: -6.94, 95% CI: -13.76, -0.13) and higher symptom burden (eg, Diarrhea: 4.63, 95% CI: -1.48, 10.74).ConclusionsThis short, web-based intervention did not appear to have an impact on PC survivors' QoL. Most men were several years past treatment for localized disease; the potential for this approach to reduce symptoms and improve QoL in men who have worse health may still be warranted
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Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.
Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies
Functional genomic landscape of acute myeloid leukaemia
The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset-accessible through the Beat AML data viewer (Vizome)-that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML
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Integrative analysis of drug response and clinical outcome in acute myeloid leukemia
Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.
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•Acute myeloid leukemia patient cohort with clinical, molecular, drug response data•Validation and discovery of diverse biological features of drug response•Broad mapping of tumor cell differentiation state affecting response to drugs•Modeling reveals a strong and targetable determinant of clinical outcome
Bottomly et al. present a functional genomic resource composed of molecular, clinical, and drug response data on acute myeloid leukemia patient specimens. Through integration of all of these data, they identify genetic and cell differentiation state features that predict drug response, and they utilize modeling to identify targetable determinants of clinical outcome