Citations

Background: Percutaneous transluminal angioplasty (PTA) is the first-line treatment for arteriovenous fistula (AVF) failure and immaturation. High rate of restenosis occurs post PTA procedure caused by venous neointimal hyperplasia (VNH), and decrease the durability of it. Failed AVF showed increased gene expression of Ier3. Poly lactic- co -glycolic acid nanoparticles (PLGA) loaded with 1,25-dihydroxyvitamin D 3 (1,25 NP) can inhibit Ier3 gene expression. Hypothesis: Adventitial delivery of 1,25 NP in hydrogel will reduce venous stenosis in a murine PTA model. Methods: Male C57BL/6J mice underwent partial nephrectomy at day -42 and arteriovenous fistula (AVF) creation at day -14. At day 0, mice were randomized to vehicle group (PTA + PLGA hydrogel) or treatment group (PTA + 1,25 NP hydrogel). Mice were euthanized at day 3 for gene expression analysis and at day 21 for histopathological analysis. Doppler ultrasound was performed weekly to evaluate AVF patency and function. Results: At day 3, PTA + 1,25 NP treated vessels had a significant decrease in the gene expression of Ier3 compared to controls. At day 21, 1,25 NP treated outflow veins had increased lumen vessel area, apoptosis, and higher peak systolic velocity, but decrease in neointimal area/media area ratio, fibroblasts, pro-inflammatory macrophages (M1), fibrosis (picrosirius red staining) and cellular proliferation. Conclusion: Adventitial delivery of 1,25 NP plus PTA has the ability to reduce restenosis and VNH formation. This protective effect is related to inhibition of Ier3 gene expression which modulates vascular inflammation and fibrosis post PTA.

Abstract Advances in genetic technology have led to the increasing use of genomic panels in precision oncology practice, with panels ranging from a couple to hundreds of genes. However, the clinical utilization and utility of oncology genomic panels, especially among vulnerable populations, is unclear. We examined the association of panel size with socioeconomic status and clinical trial matching. We retrospectively identified 9,886 eligible adult subjects in the Mayo Clinic Health System who underwent genomic testing between January 1, 2016 and June 30, 2020. Patient data were retrieved from structured and unstructured data sources of institutional collections, including cancer registries, clinical data warehouses, and clinical notes. Socioeconomic surrogates were approximated using the Area Deprivation Index (ADI) corresponding to primary residence addresses. Logistic regression was performed to analyze relationships between ADI or rural/urban status and (i) use of genomic test by panel size; (ii) clinical trial matching status. Compared with patients from the most affluent areas, patients had a lower odds of receiving a panel test (vs. a single-gene test) if from areas of higher socioeconomic deprivation [OR (95% confidence interval (CI): 0.71 (0.61–0.83), P < 0.01] or a rural area [OR (95% CI): 0.85 (0.76–0.96), P < 0.01]. Patients in areas of higher socioeconomic deprivation were less likely to be matched to clinical trials if receiving medium panel tests [(OR) (95% CI): 0.69 (0.49–0.97), P = 0.03]; however, there was no difference among patients receiving large panel tests (P > 0.05) and rural patients were almost 2x greater odds of being matched if receiving a large panel test [(OR) (95% CI): 1.76 (1.21–2.55), P < 0.01]. Significance: We identified socioeconomic and rurality disparities in the use of genomic tests and trial matching by panel size, which may have implications for equal access to targeted therapies. The lack of association between large panel tests and clinical trial matching by socioeconomic status, suggests a potential health equity impact, while removing barriers in access to large panels for rural patients may improve access to trials. However, further research is needed.

Abstract Motivation Despite the increasing evidence of utility of genomic medicine in clinical practice, systematically integrating genomic medicine information and knowledge into clinical systems with a high-level of consistency, scalability and computability remains challenging. A comprehensive terminology is required for relevant concepts and the associated knowledge model for representing relationships. In this study, we leveraged PharmGKB, a comprehensive pharmacogenomics (PGx) knowledgebase, to formulate a terminology for drug response phenotypes that can represent relationships between genetic variants and treatments. We evaluated coverage of the terminology through manual review of a randomly selected subset of 200 sentences extracted from genetic reports that contained concepts for ‘Genes and Gene Products’ and ‘Treatments’. Results Results showed that our proposed drug response phenotype terminology could cover 96% of the drug response phenotypes in genetic reports. Among 18 653 sentences that contained both ‘Genes and Gene Products’ and ‘Treatments’, 3011 sentences were able to be mapped to a drug response phenotype in our proposed terminology, among which the most discussed drug response phenotypes were response (994), sensitivity (829) and survival (332). In addition, we were able to re-analyze genetic report context incorporating the proposed terminology and enrich our previously proposed PGx knowledge model to reveal relationships between genetic variants and treatments. In conclusion, we proposed a drug response phenotype terminology that enhanced structured knowledge representation of genomic medicine. Supplementary information Supplementary data are available at Bioinformatics online.

Background To determine whether antibiotic treatment is a risk factor for immune-related adverse events (irAEs) across different patients with cancer receiving anti-PD-1/PD-L1 therapies. Methods The retrospective analysis includes clinical information from 767 patients with cancer treated at Hunan Cancer Hospital from 2017 to 2020. The pharmacovigilance data analysis includes individual cases of 38,705 safety reports from the US Food and Drug Administration Adverse Event Reporting System (FAERS) from 2014 to 2020, and 25,122 cases of safety reports from the World Health Organization database VigiBase from 2014 to 2019. All cases that received anti-PD-1/PD-L1 treatment were included. Multiomics data from patients across 25 cancer types were download from The Cancer Genome Atlas. Logistic regression and propensity score algorithm was employed to calculate OR of irAEs. Results Retrospective analysis of in-house patients showed that irAE potential risks are higher in all cancer (OR 2.12, 95% CI 1.38 to 3.22, false discovery rate (FDR) adjusted-p=1.93×10−3) and patients with lung cancer (OR 3.16, 95% CI 1.67 to 5.95, FDR adjusted-p=1.93×10−3) when using antibiotics. Potential risk of irAEs in patients with lung cancer with antibiotic treatment is significantly higher in FAERS (OR 1.39, 95% CI 1.21 to 1.59; FDR adjusted-p=1.62×10−5) and VigiBase (OR 1.32, 95% CI 1.09 to 1.59, FDR adjusted-p=0.05). Mechanistically, decreased microbial diversity caused by antibiotics use may increase the irAE risk through mediating the irAE-related factors. Conclusions Our study is the first to comprehensively demonstrate the associations of irAEs and antibiotic during anti-PD-1/PD-L1 therapy across a wide spectrum of cancers by analyzing multisource data. Administration of antibiotics should be carefully evaluated in patients with cancer treated by anti-PD-1/PD-L1 to avoid potentially increasing irAE risk.

Abstract Background: Next-generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in precision oncology practice. Due to the heterogeneity of individual patient’s disease conditions and treatment journeys, not all targeted therapies were initiated despite actionable mutations. To better understand and support the clinical decision-making process in precision oncology, there is a need to examine real-world associations of patients’ genetic information and treatment choice.Methods: To fill the gap of insufficient use of real-world data (RWD) in electronic health records (EHRs), we generated a single Resource Description Framework (RDF) resource, called PO2RDF (precision oncology to RDF) by integrating information regarding gene, variant, disease, and drug from genetic reports and EHRs. Results: There are total 2,309,014 triples contained in the PO2RDF. Among them 32,815 triples are related to Gene, 34,695 triples are related to Variant, 8,787 triples are related to Disease, 26,154 triples are related to Drug. We performed one use case analysis to demonstrate the usability of the PO2RDF: we examined real-world associations between EGFR mutations and targeted therapies to confirm existing knowledge and detect off-label use. Conclusions: In conclusion, our work proposed to use RDF to organize and distribute clinical RWD that is otherwise inaccessible externally. Our work serves as a pilot study that will lead to new clinical applications and could ultimately stimulate progress in the field of precision oncology.

Deep venous thrombosis (DVT) is a severe complication of coronavirus disease 2019 (COVID-19). The purpose of this study was to study the prevalence, risk factors, anticoagulant therapy and sex differences of DVT in patients with COVID-19. The enrolled 121 hospitalized non-ventilator patients were confirmed positive for COVID-19. All suspected patients received color Doppler ultrasound (US) to screen for DVT in both lower extremities. Multivariate logistic regression was performed to identify risk factors related to DVT in COVID-19 patients. DVT was found in 48% of the asymptomatic COVID-19 patients with an increased PADUA or Caprini index using US scanning. The multivariate logistic regression determined that age (OR, 1.05; p = .0306), C-reactive protein (CRP) (OR, 1.02; p = .0040), and baseline D-dimer (OR, 1.42; p = .0010) were risk factors among COVID-19 patients. Although the most common DVT location was infrapopliteal (classes I and II), higher mortality in DVT-COVID-19 patients was confirmed. DVT-COVID-19 patients presented significant increases in CRP, neutrophil count, and D-dimer throughout the whole inpatient period compared to non-DVT-COVID-19 patients. Although anticoagulation therapy accelerated the recovery of lymphocytopenia in DVT patients, men DVT-COVID-19 patients with anticoagulant therapy showed significant higher CRP and neutrophil count vs. lymphocyte count (N/L) ratio, but showed lower lymphocyte counts compared to women DVT-COVID-19 patients. DVT is common in COVID-19 patients with high-risk factors, especially for older age and higher CRP and baseline D-dimer populations. It is important to consider sex differences in anticoagulant therapy among DVT-COVID-19 patients.

Abstract Background: Next-generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients’ genetic information and further associate treatment decisions with genetic information.Methods: We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated in a Foundation-tested women cancer cohort (N=196). Upon retrieval of patients’ genetic information using NLP system, we assessed the completeness of genetic data captured in unstructured clinical notes according to a genetic data-model. We examined the distribution of different topics regarding BRCA1/2 throughout patients’ treatment process, and then analyzed the association between BRCA1/2 mutation status and the discussion/prescription of targeted therapy. Results: We identified seven topics in clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance (VUS). Our rule-based system achieved a precision of 0.87, recall of 0.93 and F-measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F-measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F-measure of 0.82 for seven-topic classification. We found in result-containing sentences, capture of BRCA1/2 mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between BRCA1/2 positive mutation and targeted therapies.Conclusions: In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes. Data quality issues such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate real-world utility of genetic information to initiate a prescription of targeted therapy.

Abstract Background : Next- generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients’ genetic information and further associate treatment decisions with genetic information. Methods : We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated in a Foundation-tested women cancer cohort (N=196). Upon retrieval of patients’ genetic information using NLP system, we assessed the completeness of genetic data captured in unstructured clinical notes according to a genetic data-model. We examined the distribution of different topics regarding BRCA1/2 throughout patients’ treatment process, and then analyzed the association between BRCA1/2 mutation status and the discussion/prescription of targeted therapy . Results: We identified seven topics in clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance (VUS) . O ur rule-based system achieved a precision of 0.87, recall of 0.93 and F- measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F- measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F- measure of 0.82 for seven-topic classification. We found in result-containing sentences, capture of BRCA1/2 mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between BRCA1/2 positive mutation and targeted therapies. Conclusions : In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes . Data quality issues such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate real-world utility of genetic information to initiate a prescription of targeted therapy.

Background Women have decreased hemodialysis arteriovenous fistula (AVF) maturation and patency rates. We determined the mechanisms responsible for the sex‐specific differences in AVF maturation and stenosis formation by performing whole transcriptome RNA sequencing with differential gene expression and pathway analysis, histopathological changes, and in vitro cell culture experiments from male and female smooth muscle cells. Methods and Results Mice with chronic kidney disease and AVF were used. Outflow veins were evaluated for gene expression, histomorphometric analysis, Doppler ultrasound, immunohistologic analysis, and fibrosis. Primary vascular smooth muscle cells were collected from female and male aorta vessels. In female AVFs, RNA sequencing with real‐time polymerase chain reaction analysis demonstrated a significant decrease in the average gene expression of BMP7 (bone morphogenetic protein 7) and downstream IL17Rb (interleukin 17 receptor b) , with increased transforming growth factor‐β1 ( Tgf‐β1) and transforming growth factor‐β receptor 1 ( Tgfβ‐r1) . There was decreased peak velocity, negative vascular remodeling with higher venous fibrosis and an increase in synthetic vascular smooth muscle cell phenotype, decrease in proliferation, and increase in apoptosis in female outflow veins at day 28. In vitro primary vascular smooth muscle cell experiments performed under hypoxic conditions demonstrated, in female compared with male cells, that there was increased gene expression of Tgf‐β1 , Tgfβ‐r1 , and Col1 with increased migration. Conclusions In female AVFs, there is decreased gene expression of BMP7 and IL17Rb with increased Tgf‐β1 and Tgfβ‐r1 , and the cellular and vascular differences result in venous fibrosis with negative vascular remodeling.