11 research outputs found
Dendritic cell vaccines containing lymphocytes produce improved immunogenicity in patients with cancer
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RNA Identification of PRIME Cells Predicting Rheumatoid Arthritis Flares
BACKGROUND
Rheumatoid arthritis, like many inflammatory diseases, is characterized by episodes of quiescence and exacerbation (flares). The molecular events leading to flares are unknown.
METHODS
We established a clinical and technical protocol for repeated home collection of blood in patients with rheumatoid arthritis to allow for longitudinal RNA sequencing (RNA-seq). Specimens were obtained from 364 time points during eight flares over a period of 4 years in our index patient, as well as from 235 time points during flares in three additional patients. We identified transcripts that were differentially expressed before flares and compared these with data from synovial single-cell RNA-seq. Flow cytometry and sorted-blood-cell RNA-seq in additional patients were used to validate the findings.
RESULTS
Consistent changes were observed in blood transcriptional profiles 1 to 2 weeks before a rheumatoid arthritis flare. B-cell activation was followed by expansion of circulating CD45−CD31−PDPN+ preinflammatory mesenchymal, or PRIME, cells in the blood from patients with rheumatoid arthritis; these cells shared features of inflammatory synovial fibroblasts. Levels of circulating PRIME cells decreased during flares in all 4 patients, and flow cytometry and sorted-cell RNA-seq confirmed the presence of PRIME cells in 19 additional patients with rheumatoid arthritis.
CONCLUSIONS
Longitudinal genomic analysis of rheumatoid arthritis flares revealed PRIME cells in the blood during the period before a flare and suggested a model in which these cells become activated by B cells in the weeks before a flare and subsequently migrate out of the blood into the synovium. (Funded by the National Institutes of Health and others.
Response to Letter to the Editor on: Tranexamic Acid Does Not Reduce the Risk of Transfusion in Rheumatoid Arthritis Patients Undergoing Total Joint Arthroplasty
Unmet need in rheumatology: reports from the Advances in Targeted Therapies meeting, 2022.
To detail the unmet clinical and scientific needs in the field of rheumatology. After a 2-year hiatus due to the SARS-CoV-2 pandemic, the 22nd annual international Advances in Targeted Therapies meeting brought together more than 100 leading basic scientists and clinical researchers in rheumatology, immunology, epidemiology, molecular biology and other specialties. Breakout sessions were convened with experts in five rheumatological disease-specific groups including: rheumatoid arthritis (RA), psoriatic arthritis, axial spondyloarthritis, systemic lupus erythematosus and connective tissue diseases (CTDs). In each group, experts were asked to identify and prioritise current unmet needs in clinical and translational research, as well as highlight recent progress in meeting formerly identified unmet needs. Clinical trial design innovation was emphasised across all disease states. Within RA, developing therapies and trials for refractory disease patients remained among the most important identified unmet needs and within lupus and spondyloarthritis the need to account for disease endotypes was highlighted. The RA group also identified the need to better understand the natural history of RA, pre-RA states and the need ultimately for precision medicine. In CTD generally, experts focused on the need to better identify molecular, cellular and clinical signals of early and undifferentiated disease in order to identify novel drug targets. There remains a strong need to develop therapies and therapeutic strategies for those with treatment-refractory disease. Increasingly it is clear that we need to better understand the natural history of these diseases, including their \u27predisease\u27 states, and identify molecular signatures, including at a tissue level, which can facilitate disease diagnosis and treatment. As these unmet needs in the field of rheumatic diseases have been identified based on consensus of expert clinicians and scientists in the field, this document may serve individual researchers, institutions and industry to help prioritise their scientific activities
Sequencing and curation strategies for identifying candidate glioblastoma treatments
Abstract Background Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. Methods A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. Results WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. Conclusion These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments
Correction to: Sequencing and curation strategies for identifying candidate glioblastoma treatments
Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution
The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B and T cells. For example, a T cell HLA-DQA1 eQTL ( rs3104371 ) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.</p