4836 results for "*oma"

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• UCSF Pediatric Bithalamic Glioma Genome Project

  Brain tumors are the most common solid tumors of childhood, and the genetic drivers and optimal therapeutic strategies for many of the different subtypes remain unknown. We performed targeted next-generation sequencing of approximately 500 cancer-associated genes on a cohort of 13 pediatric bithalamic diffuse gliomas, a lethal brain tumor of childhood for which the genetic basis is largely unknown. We identified that bithalamic diffuse gliomas harbor frequent mutations in the EGFR oncogene in the absence of accompanying gene amplification and only rare histone H3 mutation. These EGFR mutations were either small in-frame insertions within exon 20 (intracellular tyrosine kinase domain) or missense mutations within exon 7 (extracellular ligand-binding domain). Accompanying alterations included frequent TP53 mutation, CDK6 amplification or CDKN2C mutation, and BCOR and BCORL1 mutation or deletion.

  Study EGAS00001004033

• Clear cell sarcoma sequencing data

  This study shares DNA and RNA sequencing data from Clear cell sarcoma patients

  Study EGAS00001006072

• Whole genome sequencing with linked reads of pediatric glioblastoma samples

  Pediatric glioblastoma (pGBM) is a lethal cancer with no effective therapies. To understand mechanisms of tumor evolution in this cancer, we performed whole genome sequencing with linked reads on longitudinally resected pGBM samples. Our analyses showed that all diagnostic and recurrent samples were collections of genetically diverse subclones. Clonal composition rapidly evolved at recurrence, with less than 8% of non-synonymous single nucleotide variants being shared in diagnostic-recurrent pairs. In order to track the origins of the mutational events we observed in pGBM, we generated whole genome datasets for two patients and their parents. These trios showed that genetic variants could be (i) somatic, (ii) inherited from a healthy parent, or (iii) arose de novo in the germlines of pGBM patients. Analysis of variant allele frequencies supported a model of tumor growth involving slow-cycling cancer stem cells that give rise to fast-proliferating progenitor-like cells and to non-dividing cells. Interestingly, radiation and anti-mitotic chemotherapeutics did not increase overall tumor burden upon recurrence. These findings support an important role for slow-cycling stem cell populations in contributing to recurrences, since slow-cycling cell populations are expected to be less prone to genotoxic stress induced by these treatments and to accumulate few mutations. Our results highlight the need for new targeted treatments that account for the complex functional hierarchies and genomic heterogeneity of pGBM.

  Study EGAS00001003432

• Transcriptional mechanisms of resistance to anti-PD-1 therapy

  Purpose: To explore factors associated with response and resistance to anti-PD-1 therapy, we analyzed multiple disease sites at autopsy in a patient with widely metastatic melanoma who had a heterogeneous response.Materials and Methods: Twenty-six melanoma specimens (four pre-mortem, 22 post-mortem) were subjected to whole-exome sequencing. Candidate immunologic markers and gene expression were assessed in ten cutaneous metastases showing response or progression during therapy.Results: The melanoma was driven by biallelic inactivation of NF1. All lesions had highly concordant mutational profiles and copy number alterations, indicating linear clonal evolution. Expression of candidate immunologic markers was similar in responding and progressing lesions. However, progressing cutaneous metastases were associated with over-expression of genes associated with extracellular matrix and neutrophil function.Conclusions: Although mutational and immunologic differences have been proposed as the primary determinants of heterogeneous response/resistance to targeted therapies and immunotherapies, respectively, differential lesional gene expression profiles may also dictate anti-PD-1 outcomes.

  Study EGAS00001002195

• 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

• Adenoma to Carcinoma transition in Colorectal Cancer

  Although colorectal carcinoma development is well accepted as a step-wise and sequential process, it is commonly investigated using non-sequential adenoma and carcinoma samples. Here we compared molecular pathologies of the earliest carcinoma stages with those in their adenomatous precursors to unravel common and divergent genetic traits between matched neoplasms with a shared evolutionary history but different contemporary biology.

  Study EGAS00001003468

• An Unusual Genomic Variant of Pancreatic Ductal Adenocarcinoma with an Indolent Clinical Course

  We describe a patient with biopsy-proven locally advanced pancreatic ductal adenocarcinoma (PDA) who underwent a modified course of gemcitabine and stereotactic body radiation therapy. Whole exome sequencing of this patient's tumor revealed a simple genome landscape with no evidence of mutations, copy number changes, or structural alterations in genes most commonly associated with PDA, i.e. KRAS, CDKN2A, TP53 or SMAD4. An analysis of germline DNA revealed no pathogenic variants of significance. Whole exome and whole genome sequencing identified a somatic mutation of RNF213 and an inversion/deletion of CTNNA2 as the genetic basis of this PDA. Although PDA is classically characterized by a predictable set of mutations, these data suggest that alternate genetic paths to PDA may exist, which can be associated with a more indolent clinical course.

  Study EGAS00001002192

• The genomic landscape of follicular and diffuse large B-cell lymphoma

  As part of the ICGC, the ICGC MMML-Seq project performed whole genome sequencing and RNA sequencing of follicular and diffuse large B-cell lymphoma. Follicular and diffuse large B-cell lymphomas are the most common B-cell lymphomas in adulthood. Analyses were performed in concordance with the guidelines of the ICGC. The results define the genomic landscape of structural variants, somatic single nucleotide variants and mutational signatures in these lymphomas

  Study EGAS00001002199

• Circumventing intratumoral heterogeneity to identify potential therapeutic targets in hepatocellular carcinoma. Details please contact lifuqiang@genomics and zhaoxin@genomics.cn.

  Background and aims: Intratumoral heterogeneity (ITH) challenges identifying mutations with target therapy potential whereas circulating cell-free DNAs (cfDNAs) could reflect nearly the entire mutation spectrum in given tumors. We investigated how to minimize the limit of ITH for profiling hepatocellular carcinoma (HCC).Methods: Thirty-two multi-regional HCC samples from five patients were subjected to whole exome sequencing (WES) and targeted deep sequencing (TDS). ITH extent was measured by the average percentage of non-ubiquitous mutations (present in parts of tumor regions). Matched cfDNAs were also analyzed by WES and TDS. Profiling efficiencies of single tumor specimen and cfDNA were compared and the one better depicted mutational landscape was selected to screen therapeutic targets.Results: We found variable extents of ITH in HCCs and observed branched and parallel evolution patterns. ITH level decreased at higher sequencing depth of TDS than that measured by WES (28.1% vs 34.9%, P < 0.01) but it remained unchanged upon additional samples analyzed. TDS of single tumor specimen detected an average of 70% the total mutations in HCC. Although more mutations were detected in cfDNA under TDS than WES, an average of 47.2% total HCC mutations uncovered by cfDNA suggested tissue outperform cfDNA and the latter may serve as alternative in profiling HCC genome. Consequently, TDS of single tumor tissue in 66 patients and cfDNAs in four unresectable HCCs identified 38.6% (26/66 and 1/4) patients bearing therapeutic targets.Conclusions: TDS of single tumor specimen could largely circumvent ITH to uncover mutations indicative of target therapy in HCC.

  Study EGAS00001002207

• Revealing active mutational processes in tumours using DigiPico/MutLX at unprecedented accuracy

  Active mutational processes in a tumor result in genetic micro-heterogeneity that can determine the tumor’s evolutionary trajectory. Identification of these processes by studying micro-heterogeneity can unveil novel aspects of tumor evolution with potential therapeutic implications. Such studies, however, are inherently problematic because of the discovery of excessive false positive mutations. Here we report the optimization and validation of a robust whole genome sequencing and analysis pipeline (DigiPico/MutLX) that virtually eliminates false positive results. Using our method, we identified, for the first time, a sub-clonal local hyper-mutation (kataegis) event in a recurrent ovarian carcinoma, which was unidentifiable from the bulk WGS data of the same tumor. Overall, we propose DigiPico/MutLX method as a powerful framework for the reliable study of genetic micro-heterogeneity in tumor and normal tissues.

  Study EGAS00001003555