690 results for ""PhD+2001"+"Professor+of+Bioinformatics"+2012+18000+citations+profile"

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• Autism Sequencing Consortium (ASC)

  The ARRA Autism Sequencing Collaboration was created in 2010 bringing together expert large-scale sequencing center (at the Baylor College of Medicine, PI Richard Gibbs and the Board Institute of MIT and Harvard, PI Mark J. Daly) and a collaborative network of research labs focused on the genetics of autism (brought together by the Autism Genome Project and the Autism Consortium). These groups worked together to utilize dramatic new advances in DNA sequencing technology to reveal the genetic architecture of autism through comprehensive examination of the exotic sequence of all genes. The Autism Sequencing Consortium (ASC) was founded by Joseph D. Buxbaum and colleagues as an international group of scientists who share autism spectrum disorder (ASD) samples and genetic data. The PIs are Drs. Joseph D. Buxbaum (Icahn School of Medicine at Mount Sinai), Mark J. Daly (Broad Institute of MIT and Harvard), Bernie Devlin (University of Pittsburgh School of Medicine), Kathryn Roeder (Carnegie Mellon University, Matthew State and Stephan Sanders (University of California, San Francisco). The rationale for the ASC is described in Buxbaum et al. 2012, and this paper should be cited when referencing the data set. All shared data and analysis is hosted at a single site, which enables joint analysis of large-scale data from many groups. The ASC was first supported by a cooperative agreement grant to four lead sites funded by the National Institute of Mental Health (U01MH100233, U01MH100209, U01MH100229, U01MH100239), with additional support from the National Human Genome Research Institute. The NIMH recently renewed their support with a second grant (U01MH111661, U01MH111660, U01MH111658 and U01MH111662) to expand the project from 29,000 genomes to more than 50,000 exomes over the next 5 years. NHGRI provides ongoing sequencing support for the ASD through the Broad Center for Common Disease Genomics (UM1HG008895, Mark Daly, PI).

  Study phs000298

• Heart Failure Network - Effectiveness of Ultrafiltration in Treating People with Acute Decompensated Heart Failure and Cardiorenal Syndrome (HFN CARRESS - BioLINCC)

  ObjectiveThe CARRESS trial examined the effectiveness of ultrafiltration compared with a strategy of diuretic-based stepped pharmacologic therapy on renal function and weight loss in patients with heart failure who have worsening renal function and persistent congestion.BackgroundAcute cardiorenal syndrome is defined as worsening renal function in patients with acute decompensated heart failure. It occurs in 25 to 33% of patients and is associated with poor outcomes. Current heart failure treatments focus on removing excess fluid buildup, however administration of intravenous diuretics may contribute to worsening renal function. Venovenous ultrafiltration is one alternative therapy that may be effective in patients with acute decompensated heart failure complicated by acute cardiorenal syndrome and persistent congestionSubjectsA total of 188 patients were randomized.DesignHospitalized subjects were randomly assigned to receive either ultrafiltration therapy or pharmacologic therapy. Ultrafiltration was performed at a fluid-removal rate of 200 ml per hour and loop diuretics were discontinued for the duration of the intervention. The addition of intravenous vasodilators or positive inotropic agents was prohibited, unless they were deemed to be necessary as rescue therapy. Patients assigned to stepped pharmacologic therapy received intravenous diuretics to manage congestion and maintain a urine output of 3 to 5 liters per day. In both groups, the assigned treatment strategy was to be continued until the signs and symptoms of congestion in the patient were reduced as much as possible. The primary end points were baseline changes in serum creatinine and weight as assessed at 96 hours after treatment assignment.ConclusionsThe use of a stepped pharmacologic-therapy algorithm was superior to a strategy of ultrafiltration for the preservation of renal function, and there was a similar amount of weight loss between the two groups. Ultrafiltration was associated with a higher rate of adverse events (Bart, et al., 2012, PMID: 23131078).

  Study phs003510

• McGill Epigenomics Mapping Centre

  The McGill Epigenomics Mapping Centre (EMC) and Data Coordination Centre (EDCC) were established in 2012 at the McGill University and Genome Quebec Innovation Centre (Montreal, Canada) to support large-scale human epigenome mapping for a broad spectrum of cell types and diseases. Previous epigenetic studies have provided profiles of specific chromatin marks in relation to basic biological processes, and future studies of molecular mechanisms must build upon these proofs-of-concept by integrating sequence-based variation with multiple levels of epigenetic and transcriptional regulation across the genome of human tissues and animal disease models. This project leverages the high-throughput sequencing infrastructure and expertise in genomics and transcriptomics at the McGill Innovation Centre to carry out this research. Controlled access to the data by collaborators and the greater scientific community is gained via a portal, which takes advantage of Compute Canada high-performance computing cluster resources to manage the large volume of data associated from the generation of reference epigenome maps. McGill University is one of two Canadian mapping and data coordination centres, the other based at the Michael Smith Genome Sciences Centre in Vancouver, British Columbia. The generation of comprehensive epigenome maps at McGill University is part of a larger international effort that is coordinated by the International Human Epigenome Consortium (IHEC), whose overall long-term objective is to determine the extent to which the epigenome shapes human populations over generations and in response to the environment. This project is funded under the Canadian Epigenetics, Environment, and Health Research Consortium (CEEHRC) by the Canadian Institutes of Health Research and by Genome Quebec, with additional support from Genome Canada. The computing and networking infrastructure, and part of the software development, are provided by Compute Canada and CANARIE.

  Study EGAS00001000995

• Single Cell RNA-Seq Reveals Malignant and Stromal Programs Associated with Metastasis in Head and Neck Cancer

  This study of head and neck cancer aimed to profile individual malignant, stromal, and immune cells in treatment-naive oral cavity squamous cell carcinoma, with or without matched metastatic cervical lymph nodes. Clinical and pathologic information was collected on all patients, in addition to a fresh biopsy sample from the primary tumor and/or metastatic lymph node at the time of initial surgery. Biopsy specimens were processed for single cell RNA-seq (scRNA-seq) and/or whole exome sequencing (WES). A total of ~6,000 single cells from 18 participants were included in the final dataset. Stromal and immune cells had consistent expression programs across patients. Conversely, malignant cells varied within and between tumors in their expression of signatures related to cell cycle, stress, hypoxia, epithelial differentiation, and partial epithelial-to-mesenchymal transition (p-EMT). One of the top scoring genes in this program was TGFβ-induced (TGFBI), implicating the classic EMT regulator TGFβ. Cells expressing the p-EMT program spatially localized to the leading edge of primary tumors. By integrating single-cell transcriptomes with bulk expression profiles for hundreds of tumors, we refined Head and Neck Squamous Cell Carcinoma (HNSCC) subtypes by their malignant and stromal composition, and established p-EMT as an independent predictor of nodal metastasis, grade, and adverse pathologic features. Our results provide insight into the HNSCC ecosystem and define stromal interactions and a p-EMT program associated with metastasis.

  Study phs001474

• Machine learning to detect the SINEs of cancer

  We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA (cfDNA) through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a novel machine-learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to samples from 5108 individuals, 2037 with cancer and the remainder without cancer. Samples from cancer patients and controls were pre-specified into four cohorts used for: 1) model training, 2) analyte integration and threshold determination, 3) validation, and 4) reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the Validation Cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and 8 common protein biomarkers detected 51% of 1167 cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature – the global reduction of AluS sub-family elements in the circulating DNA of cancer patients. We confirmed this reduction through the analysis of another independent dataset obtained with a very different approach (whole genome sequencing). The evaluation of Alu elements therefore has the potential to enhance the performance of several methods designed for the earlier detection of cancer.

  Study EGAS00001007169

• HuBMAP: A Spatially Resolved Molecular Atlas of Human Endothelium

  The human circulatory system plays a vital role in the proper functioning of every organ in the human body. Because vascular related diseases, such as atherosclerosis, do not occur homogeneously throughout the vascular tree, differences in the endothelium of various organs must exist. There is even evidence of heterogeneity of adjacent endothelial cells. We are studying normal endothelium throughout the body and across individuals of different ages to characterize these differences. The first step will be to procure normal tissue samples from a wide range of organs. From disaggregated samples of these tissues, we will perform single cell combinatorial indexing (sci-) to generate chromatin accessibility (sci-ATAC-seq) and transcriptome (sci-RNA-seq) data from millions of single cells across hundreds of samples.  Molecular heterogeneity might correlate with multiple facets of the anatomical distribution of endothelium, including vascular vs. lymphatic, arteries vs. veins, capillaries vs. larger vessels, differences between organs, organ zonation, cell-to-cell differences, etc. Based on heterogeneous markers defined from deep profiling of disaggregated cells, we will employ seqFISH to spatially relate the distinct endothelial subpopulations arising from the single nuclei data to histologic preparations. For example, this will allow us to identify subpopulations with particular markers as arterial, capillary, venous, or lymphatic. The end product will include multiparameter images in which the complete gene expression and chromatin accessibility profile can be interactively visualized for every cell.

  Study phs002267

• Plasma-Seq of patients with metastatic prostate cancer

  Study 1 In this study we analysed patients with metastatic prostate cancer to scan their tumor genomes noninvasively in plasma DNA. We wanted to make whole-genome analysis from plasma DNA amenable to clinical routine applications and developed an approach based on a benchtop high-throughput platform, i.e. Illuminas MiSeq instrument. We performed whole-genome sequencing from plasma at a shallow sequencing depth to establish a genome-wide copy number profile of the tumor at low costs within 2 days. The genome-wide profiling in the plasma of our patients revealed multiple copy number aberrations including those previously reported in prostate tumors, such as losses in 8p and gains in 8q. High-level copy number gains in the AR locus were observed in patients with CRPC but not with CSPC disease. We identified the TMPRSS2-ERG rearrangement associated 3-Mbp deletion on chromosome 21 and found corresponding fusion plasma fragments in these cases. In an index case multiregional sequencing of the primary tumor identified different copy number changes in each sector, suggesting multifocal disease. Our plasma analyses of this index case, performed 13 years after resection of the primary tumor, revealed novel chromosomal rearrangements, which were stable in serial plasma analyses over a 9 months period, which is consistent with the presence of one metastatic clone. Our approach provides specific genomic signatures within 2 days which may therefore serve as “liquid biopsy”.

  Study EGAS00001000451

• Ultra-sensitive tumor-informed ctDNA monitoring of treatment response in advanced esophagogastric cancer patients

  To explore whether ultra-sensitive circulating tumor DNA (ctDNA) profiling enables earlier prediction of treatment response and detection of disease progression, we applied NeXT Personal, an ultra-sensitive bespoke tumor-informed liquid biopsy platform, to profile tumor samples from the KeyLargo study, a phase II trial in which metastatic esophagogastric cancer (mEGC) patients received capecitabine, oxaliplatin, and pembrolizumab. A total of 24 patients were evaluated, and all were ctDNA-positive at baseline. ctDNA levels varied from 406,067 down to 1.5 parts per million (PPM) with a median limit of detection of 2.03 PPM. ctDNA dynamics were highly correlated with changes in tumor size (ρ = 0.59, p = 7.3×10-9). Lack of early molecular response (50% or greater decrease in ctDNA levels at first available time point after 30 days, C2D1 or C4D1) was associated with worse overall survival (OS) (HR 6.6, 95% CI 1.8-24.1, p = 0.005) and progression-free survival (PFS) (HR 15.4, 95% CI 2.7-87.0, p = 0.002). Lack of molecular clearance of ctDNA was associated with worse OS (HR 6.9, 95% CI 1.5-30.8, p = 0.012) and PFS (HR 19.2, 95% CI 2.4-152.8, p = 0.005). Molecular progression (ctDNA increase) preceded imaging-derived progression by a median lead time of 65 days. These results suggest that ultra-sensitive liquid biopsy approaches could improve treatment decision-making for mEGC patients receiving chemotherapy and immunotherapy.

  Study EGAS50000001224

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452

• Efficacy of JAK/STAT pathway inhibition in murine xenograft models of early T-cell precursor (ETP) acute lymphoblastic leukemia

  Early T-cell precursor (ETP) acute lymphoblastic leukemia (ALL) is a recently described subtype of T-ALL characterized by a unique immunophenotype and genomic profile as well as a high rate of induction failure. Frequent mutations in cytokine receptor and JAK/STAT signaling pathways led us to hypothesize that ETP-ALL is dependent on JAK/STAT signaling. Here we demonstrate aberrant activation of the JAK/STAT pathway in ETP-ALL blasts relative to non-ETP T-ALL. Moreover, ETP-ALL showed hyperactivation of STAT5 in response to IL7, an effect that was abrogated by the JAK1/2 inhibitor ruxolitinib. In vivo, ruxolitinib displayed activity in 6/6 patient-derived murine xenograft models of ETP-ALL, with profound single-agent efficacy in 5 models. Ruxolitinib treatment decreased peripheral blast counts relative to pre-treatment levels and compared to control (P<0.01) in 5/6 ETP-ALL xenografts, with marked reduction in mean splenic blast counts (P<0.01) in 6/6 samples. Surprisingly, both JAK/STAT pathway activation and ruxolitinib efficacy were independent of the presence of JAK/STAT pathway mutations, raising the possibility that the therapeutic potential of ruxolitinib in ETP-ALL extends beyond those cases with JAK mutations. These findings establish the preclinical in vivo efficacy of ruxolitinib in ETP-ALL, a biologically distinct subtype for which novel therapies are needed.

  Study EGAS00001001146