3311 results for "*geneti*"

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• Epigenetic landscape of mixed phenotype leukemias

  Leukemias with ambiguous lineage comprise a number of loosely defined entities, often without a clear mechanistic basis. Here, we investigated a group of such leukemias with a CpG Island Methylator Phenotype (CIMP), previously identified as CEBPA-silenced AML. Transcriptomics and epigenomics analyses revealed a hybrid myeloid/lymphoid epigenetic landscape, whereas genetic alterations were heterogenous. This suggests that CIMP leukemias are defined by their shared epigenetic profile rather than a common genetic lesion. Gene expression enrichment showed strong similarity with ETP-ALL and an early lymphoid progenitor cell of origin. Accordingly, integration of differential methylation and expression revealed widespread silencing of myeloid transcription factors (TFs), among which CEBPA was key for differentiation arrest. Hypermethylation also resulted in loss of CTCF binding, accompanied by a few changes in chromatin interactions involving critical TFs like KLF4. In conclusion, epigenetic dysregulation, and not genetic lesions, explain the mixed phenotype of a group of CIMP leukemias resembling ETP-ALL.

  Study EGAS00001007094

• DNA-methylation variability in normal mucosa of patients with adenomatous polyps: a marker of field cancerization

  A study linking DNA methylation modifications in the normal mucosa of the right colon in individuals with adenomatous polyp formation

  Study EGAS00001007666

• Plasma pQTLs in INTERVAL cohort

  Human pQTLs using UK INTERVAL (Cambridge CEU) plasma samples. pQTL mapping of 3,283 protein analytes assayed using SomaLogic SOMAscan, across 10.6M imputed genetic variants, in 3,301 samples.

  Study EGAS00001002555

• The PEMDAC phase 2 study of pembrolizumab and entinostat in patients with metastatic uveal melanoma

  Patients with metastatic uveal melanoma (UM) have an abysmal prognosis. Preclinical studies have suggested that epigenetic therapy could enhance immunogenicity of cancer cells. Here we test if epigenetic therapy would enhance PD-1 immunotherapy in patients with metastatic UM. We report the results of the PEMDAC phase 2 clinical trial (n=29; NCT02697630) where the HDAC inhibitor entinostat was combined with the PD-1 inhibitor pembrolizumab in patients with metastatic UM. The primary endpoint was objective response rate (ORR), and was met with an ORR of 14%. The clinical benefit rate at 18 weeks was 28%, median progression free survival was 2.1 months and the median overall survival was 13.4 months. Toxicities were manageable, and there were no treatment-related deaths. Extensive genomics studies were performed using DNA/RNA and single cell sequencing and flow cytometry. Objective responses and prolonged survival were seen in three patients with BAP1 wildtype tumors, and in one patient with an iris melanoma that exhibited a UV signature. Longer survival also correlated with low baseline ctDNA levels or LDH. In conclusion, HDAC inhibition and anti-PD1 immunotherapy results in durable responses in a subset of patients with metastatic UM. Further exploration of combined immunotherapy and epigenetic therapy in metastatic UM is warranted.

  Study EGAS00001005478

• The Proteogenomic Subtypes of Acute Myeloid Leukemia

  Acute myeloid leukemia (AML) is an aggressive blood cancer with poor prognosis. We report a comprehensive proteogenomic analysis of bone-marrow biopsies from 252 uniformly treated AML patients to elucidate the molecular pathophysiology of AML in order to inform future diagnostic and therapeutic approaches. In addition to in-depth quantitative proteomics, our analysis includes cytogenetic profiling and DNA/RNA sequencing. We identify five proteomic AML subtypes, each reflecting specific biological features spanning genomic boundaries. Two of these proteomic subtypes correlate with patient outcome, but none are exclusively associated with specific genomic aberrations. Remarkably, one subtype (Mito-AML), which is only captured in the proteome, is characterized by high expression of mitochondrial proteins and confers poor outcome, with reduced remission rate and shorter overall survival upon treatment with intensive induction chemotherapy. Functional analyses reveal that Mito-AML is metabolically wired towards stronger complex I dependent respiration and is more responsive to treatment with the BCL2 inhibitor venetoclax.

  Study EGAS00001005950

• Epigenetic reprogramming shapes monocytes and heterologous T cell derived cytokine responses in BCG vaccination

  Study EGAS00001007498

• X chromosome dosage and the genetic impact across human tissues

  Study EGAS00001006996

• X chromosome dosage and the impact on the methylation pattern across human tissues

  Study EGAS00001007020

• DNA hypermethylation and differential gene expression associated with Klinefelter syndrome

  The molecular basis for the phenotypic traits and morbidity in Klinefelter syndrome (KS) are not clarified. As DNA methylation affect gene expression and thereby play a role in disease susceptibility, we performed genome-wide DNA methylation profiling of leucocytes from peripheral blood samples from 67 KS patients, 67 male controls and 33 female controls, in addition to genome-wide RNA-sequencing profiling in a subset of 9 KS patients, 9 control males and 13 female controls. Characterization of the methylome as well as the transcriptome of both coding and non-coding genes identified a unique epigenetic and genetic landscape of both autosomal chromosomes as well as the X chromosome in KS. A subset of genes show significant correlation between methylation values and expression values. Gene set enrichment analysis of differentially methylated positions yielded terms associated with well-known comorbidities seen in KS. In addition, differentially expressed genes revealed enrichment for genes involved in the immune system, wnt-signaling pathway and neuron development. Based on our data we point towards many new candidate genes (AKAP17A, AMOT, APOB, DACT1, DDX58, DOCK7, EIF2S3, FIGNL1, G3BP1, HENMT1, HERC5, IFI44, IFI44L, IFIT1, IFIT3, ISG15, KANK1, LGALS1, NSD1, PEX10, PLSCR1, RSAD2, SHROOM2, SLC25A6, SPEG, SPON2, TXLNG), which may be implicated in the phenotype and further point towards non-coding genes (RP13-216E22.4, RP13-36G14.4, G087825, G088512), which may be involved in X chromosome inactivation in KS and in the regulation of escape genes.

  Study EGAS00001002797

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444