7249 results for "canc*"

in 16.78 milliseconds.

• QSEA – modelling of genome-wide DNA methylation from sequencing enrichment experiments

  We describe a new method, QSEA, for analyzing methylation enrichment data. We generated a benchmark experiment consisting of MeDIP-seq enrichment data, targeted BS-seq validation data, and RNA-seq data on five pairs of tumor and adjacent normal samples of non-small cell lung cancer patients. Details of the method and its performance on the data have been described in Lienhard et al. (2016).

  Study EGAS00001001822

• Breast_cancer_topographs

  Breast cancer topography will be explored using a combination of multi-region whole geome sequencing and in-situ RNA sequencing of fixed tissue specimens.

  Study EGAS00001003698

• Renal_habitat_WXS

  This project aims at correlating genetic diversity (as well as transcriptomic, metabolic and immune diversity) observed in multiple targeted tumour biopsies and circulating tumour DNA with heterogeneity on multiparametric MRI. To this end, 20 patients with RCC (15 patients with localised/locally advanced disease and 5 with mRCC) will undergo multiparametric MRI including metabolic assessment by 13C-MRSI as well as measurements of tumour perfusion, oxygenation and water diffusion. Based on unique combinations of these imaging parameters, tumour habitats will be defined through cluster mapping. Intra-operative biopsy and immediate freezing of tumour cores from different imaging habitats will be performed. Multiregional ex-vivo tumour biopsies will be spatially registered to the MR data by using 3D printed tumour moulds generated from high-resolution volumetric sequences. These moulds allow slicing tumour specimens in identical planes to the MR acquisition and increase the accuracy of tissue sampling. The biopsies will then undergo molecular profiling such as metabolomic and RNA sequencing (including mRNA immune profiling), as well as DNA sequencing to assess driver and clonal mutations in different tumour habitats and guide design of appropriate cfDNA probes. The targeted cfDNA capture panel will be employed to determine the ability of cfDNA to capture the full genetic diversity observed in the primary tumour and to relate cfDNA to various imaging habitats in both renal and pancreatic cancer. Data integration will be undertaken between imaging habitats and molecular profiles for each region.

  Study EGAS00001003703

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• RNA-seq consisting of FASTQ paired-end reads from cancer samples

  Efforts to precisely identify tumor human leukocyte antigen presented peptides (HLAp) capable of mediating T cell based tumor rejection still face important challenges. Recent reports suggest that non-canonical cancer HLAp could be immunogenic but their identification requires highly sensitive and accurate mass-spectrometry (MS)-based proteogenomics approaches. Here, we present a MS-based analytical pipeline that can precisely characterize the non-canonical HLAp repertoire, incorporating whole exome sequencing, bulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS search tools. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLAp. Albeit often at low levels and in distinct subpopulations of cells, numerous non-canonical HLAp are shared across tumors. This analytical platform holds great promise for the discovery of novel cancer antigens for cancer immunotherapy.

  Study EGAS00001003724

• Whole exome sequencing of Finnish hereditary breast cancer families

  A remarkable proportion of factors causing genetic predisposition to breast cancer (BC) are unknown in non-BRCA1/2 families. Exome sequencing was performed for 13 high-risk Finnish hereditary breast and/or ovarian cancer (HBOC) families to detect variants contributing to BC susceptibility

  Study EGAS00001001835

• Identification of 19 novel loci reveals gene regulatory mechanisms determining susceptibility to testicular germ cell tumour

  Testicular germ cell tumour (TGCT) is the most common cancer in young men1,2, and is characterised by strong inherited genetic risk factors3. Here we have undertaken large-scale genome wide association study (GWAS) for TGCT, encompassing ~7,500 cases and ~23,000 controls, through the Oncoarray consortium. We identified 19 novel loci, approximately doubling the number of known TGCT risk loci to 44 (P<5x10-8)4-14 and conduct deep, high-throughput functional annotation of all risk loci. We establish a network of physical interactions for all risk SNPs to candidate casual genes in 3D space, using high-throughput chromosome conformation capture techniques (HiC) in TGCT cells. Firstly, functional evidence reveals widespread disruption of developmental transcriptional regulators, consistent with failed primordial germ cell differentiation as an initiating step in TGCT oncogenesis15. We secondly observe multiple risk loci associated with defective microtubule assembly, compatible with the high level of aneuploidy observed in TGCTs, suggesting gross chromosomal instability. Finally KIT-MAPK signalling features as a recurrently dysregulated pathway. In summary our findings substantially increase the number of known TGCT risk alleles, and provides a functional basis for disease susceptibility.

  Study EGAS00001001836

• Mutated H3 Histones Drive Human Pre-Leukemic Hematopoietic Stem Cell Expansion And Promote Leukemic Aggressiveness

  Leukemogenesis is a stepwise progression from mutated, pre-neoplastic hematopoietic stem cells (HSCs) to full-blown leukemia. Our ability to prevent or treat acute myeloid leukemia (AML) is limited by our incomplete understanding of the epigenetic disruption that is central to this process, including improper histone methylation. Comprehensive analysis of 16 histone H3 genes in 434 primary AML samples identified Q69H, A26P, R2Q, R8H and K27M/I mutations (1.6%), and a higher incidence in secondary AML (s-AML) (9%). We establish that these mutations are important early events in leukemogenesis. They occur in pre-leukemic HSCs, increase the frequency of functional human HSCs, and alter differentiation. The mutations are present in the major leukemic clones in primary samples, and the mutant histones amplify leukemic aggressiveness with increased proliferation, expansion of leukemic progenitor and blast cells, and superior competitiveness in vivo. These effects are dependent on the specific mutation. Genome-wide analysis of K27 mutants revealed increased expression of genes involved in erythrocyte and myeloid differentiation with a corresponding decrease in histone H3 K27 tri-methylation and increase in K27 acetylation. The functional impact of histone mutations is independent of RUNX1 mutations, although they can co-occur. These data establish the involvement of H3 mutations as initial drivers of pre-cancerous stem cell expansion and leukemogenesis.

  Study EGAS00001003288

• Cancer and germline exomes consisting of FASTQ paired-end reads from melanoma and lung cancer samples

  Efforts to precisely identify tumor human leukocyte antigen presented peptides (HLAp) capable of mediating T cell based tumor rejection still face important challenges. Recent reports suggest that non-canonical cancer HLAp could be immunogenic but their identification requires highly sensitive and accurate mass-spectrometry (MS)-based proteogenomics approaches. Here, we present a MS-based analytical pipeline that can precisely characterize the non-canonical HLAp repertoire, incorporating whole exome sequencing, bulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS search tools. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLAp. Albeit often at low levels and in distinct subpopulations of cells, numerous non-canonical HLAp are shared across tumors. This analytical platform holds great promise for the discovery of novel cancer antigens for cancer immunotherapy.

  Study EGAS00001003723

• Infant_Spindle_Tumour_Study

  We aim to describe the transcriptomic landscape of infant spindle cell tumours.

  Study EGAS00001002935