15075 results for "cancer studies using rna-seq"

in 19.83 milliseconds.

• Immune and clinicopathological features predict HER2-positive breast cancer prognosis in the neoadjuvant NeoALTTO and CALGB 40601 trials

  The identification of prognostic drivers in patients receiving neoadjuvant therapy is crucial for treatment optimization in HER2-positive breast cancer, with the immune microenvironment being a key factor. Here, we investigated the complexity of B and T cell receptor (BCR and TCR) repertoires, reflecting antigen-specific immune response, in the context of two phase III trials, namely NeoALTTO and CALGB 40601, evaluating neoadjuvant paclitaxel with trastuzumab and/or lapatinib in women affected by HER2-positive breast cancer. BCR/TCR features were heterogeneous, and BCR measures describing clonal expansion demonstrated independent prognostic value. A model developed in NeoALTTO and validated in CALGB 40601 predicted event-free survival (EFS) by integrating hormone receptor and clinical nodal status, treatment response (breast pCR)breast pathological complete response (pCR) as well as tumor-infiltrating lymphocytes and BCR evenness, measuring the equality of BCR clonal distribution. The HER2-EveNT score derived from the model allows the identification of patients with 5-year EFS > 90%, and, in those not achieving pCR, of a subgroup of immune-enriched tumors with an excellent outcome despite residual disease.

  Study EGAS00001007563

• Reference epigenomes generated as part of the International Human Epigenomics Consortium (IHEC)

  The Centre for Epigenome Mapping Technologies (CEMT) is an epigenome sequencing platform funded by the Canadian Institutes of Health Research (CIHR) and Genome BC as part of the Canadian Epigenetics, Environment and Health Research Consortium (CEEHRC). CEMT aims to produce 100 human reference epigenomes, each comprising the following datasets: whole genome; whole genome bisulfite and oxidative bisulfite; transcriptome; microRNA; and chromatin immunoprecipitation (ChIP)-seq for the core chromatin marks (H3K4me1, H3K4me3, H3K9me3, H3K27me3, H3K36me3, and H3K27ac).CEEHRC is a full member of the International Human Epigenomics Consortium (IHEC), which aims to coordinate the production of 1,000 reference maps of human epigenomes for key cellular states relevant to health and diseases. As such, all datasets submitted by the CEMT platform conform to IHEC assay standards and metadata guidelines.

  Study EGAS00001000552

• A cycling, progenitor-like cell population at the root of atypical teratoid rhabdoid tumor subtype differentiation trajectories.

  Background: Atypical teratoid rhabdoid tumors (ATRTs), characterized by the loss of SMARCB1, are among the most lethal pediatric central nervous system (CNS) tumors. Three molecular subtypes have been identified, each defined by distinct molecular and clinical features. No subtype-specific treatments are available, highlighting the necessity to better understand inter- and intra-subtype heterogeneity. Methods: We generated a single-nucleus transcriptome atlas of ATRTs, validated by single-cell ATAC-seq and spatial transcriptomics, to study subtype-specific differentiation trajectories. Results: We identified brain progenitor-like expression profiles within unique subtype-specific differentiation lineages. A shared cycling, progenitor-like cell population, interspersed throughout tumors, was observed across all ATRT samples. Moreover, we demonstrate that subtype-specific differentiation trajectories can be induced pharmacologically in ATRT tumoroids directing tumor cells towards a non-proliferative, mature phenotype. Conclusions: Our study reveals that ATRT subtypes exhibit signaling trajectories mirroring normal fetal brain development, enabling the development of maturation therapies tailored towards ATRTs.

  Study EGAS00001008108

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Study EGAS00001001184

• Whole exome sequencing of gallbladder cancer

  The dataset includes the whole exome sequencing data from32 pairs of gallbladder caner tissues and patient-matched normal tissues.

  Dataset EGAD00001000901

• SAFIR02_Oncoscan

  Genomics to select patients with metastatic breast cancer for targeted therapy (microarray_oncoscan)

  Dataset EGAD00010002241

• BAM/BAI dataset of Colorectal Cancer Synthetic genomes

  This dataset contains binary alignment map (BAM) data with their respective index (BAI) from four patients synthetically generated.

  Dataset EGAD50000000316

• Michigan-Georgetown Cancer DAC

  Genomics data from cancers and/or dysplasias for the collaborations between Maureen Sartor and Nisha D'Silva at the University of Michigan and Laura Rozek at Georgetown University

  Dac EGAC50000000397

• RNAseq for 208 patients with medulloblastoma cancer (MB_COMICS cohort)

  This dataset contains 208 paired fastq files sequenced with Illumina HiSeq 2500.

  Dataset EGAD50000000600

• WES

  WES was performed on 2 TURP, 6 biopsies, 1 CDX, 1 cell line, 6 CTCs, 1 DNA germline and 1WBC from prostate cancer

  Dataset EGAD00001006009

• Organoid Derivation Pilot: RNAseq

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005001

• Multi-region WGS of a RET fusion positive cancer

  high depth WGS sequencing of 8 sites of a RET fusion tumour

  Dataset EGAD00001006878

• Illumina_RNA_T

  RNAseq on Illumina HiSeq2000/2500 of colorectal cancer primary tumor sample

  Dataset EGAD00001002077

• SCCOHT

  Exome sequencing of familial and sporadic small cell cancer of ovary cases.

  Dataset EGAD00001000791

• Prostate WGS data (late onset)

  Whole genome sequencing for 12 late onset prostate cancer tumor/control pairs (ICGC)

  Dataset EGAD00001003290

• 20180208_EGA_Trench_MetCellLine.1

  Illumina platform sequencing data of SureSelect exome libraries prepared from 3 samples from one donor: a normal, primary breast cancer, and cell line derived from metastasis

  Dataset EGAD00001003956

• OT2_Illumina_RNA_MET

  RNAseq on Illumina HiSeq2000/2500 of colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003364

• Targeted gene sequencing of gallbladder cancer

  The dataset includes the targeted gene sequencing data from51 pairs of gallbladder caner tissues and patient-matched normal tissues.

  Dataset EGAD00001000902

• GIST SSGXVIII trial targeted gene sequencing

  Targeted cancer gene sequencing of samples enrolled in the SSGXVIII trial from Finland.

  Dataset EGAD00001001427

• Whole exome sequencing data for paired non-serous endometrial and ovarian carcinomas from 27 patients with concurrent tumours.

  Dataset for Synchronous Endometrial and Ovarian Cancer

  Dataset EGAD00001015680