7406 results for "canc*"

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• Genome and transcriptome sequence data from a infantile fibrosarcoma tumor patient

  Genome and transcriptome sequence data from a infantile fibrosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015263

• Clinical and genetic factors associated with tumor response to neoadjuvant (chemo)radiotherapy, survival and recurrence risk in rectal cancer

  Dataset consists of 216 samples, with each sample having a BAM, BAI and VCF file.

  Dataset EGAD00001011297

• Neoantigen Immunogenicity Landscapes and Evolution of Tumor Ecosystems During Immunotherapy with Nivolumab

  We performed Whole Exome (WXS) and RNASeq sequencing on samples obtained from the same site before and during therapy from our prospective clinical trial (CA209-153, NCT02066636) of nivolumab in advanced Non-small cell lung cancer (NSCLC) patients that progressed on chemotherapy. There are 58 pre and 42 on therapy WXS samples and 24 pre and 12 on therapy RNASeq samples. All WXS tumor samples have matching normal samples.

  Dataset EGAD00001011302

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 8a)

  Whole genome sequencing data of 26 high-grade serous carcinoma (HGSC) patients (87 samples) sequenced with MGISEQ-2000 and HiSeq X Ten.

  Dataset EGAD00001011304

• INSPIRE multi-timepoint cfMeDIP dataset

  This dataset contains raw sequencing data from multi-timepoint cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) of plasma samples from the INSPIRE study (NCT02644369). Details about the study, including inclusion/exclusion criteria and interventions are available at https://clinicaltrials.gov/study/NCT02644369. Briefly, five cohorts were included, INS-A (head & neck squamous cell carcinoma), INS-B (triple-negative breast cancer), INS-C (high-grade serous ovarian cancer), INS-D (melanoma), and INS-E (mixed solid tumors). All patients in the study were diagnosed with advanced cancer and received treatment with pembrolizumab. Plasma samples were collected at baseline and every 3 cycles until disease progression, death, loss of follow-up, or completion of the study. Plasma samples were first processed for cell-free DNA mutations and remaining samples were then processed by cfMeDIP-seq, prioritizing baseline and post-cycle 3 samples. In total, data from 204 timepoints from 87 distinct patients are deposited.

  Dataset EGAD00001011312

• Machine Learning Guided Signal Enrichment for Plasma Tumor-burden Monitoring Dataset

  Includes 4 Datasets: 1. 35 Control Plasma WGS samples sequenced on NovaSeq V1.0 Chemistry at the New York Genome Center. Denoted by CTRL-2XX naming scheme. 2. Plasma WGS from 17 patients with Small Cell Lung Cancer. Samples extracted at either Pretreatment or Postoperative at weeks 2 or 3. Denoted by SCLC-XX naming scheme. 3. Plasma WGS from a synthetic mixing study of a high-burden melanoma plasma sample with plasma bag, at estimated tumor concentrations of 10e-3, 10e-4, 10e-5, and 10e-6 for 2 replicates. Denoted by SM-repX naming scheme. 4. Assorted Plasma, Tumor, and Normal WGS from patients with NSCLC expressing high-burden for use in model training. Denoted by NSCLC-2XX naming scheme.

  Dataset EGAD00001011352

• 10x Single Cell Gene Expression Library TENX069

  10x Single Cell Gene Expression library TENX069 for Triple negative breast cancer patient-derived xenograft SA609X4XB03083

  Dataset EGAD00001011211

• Cancer treatment re-shapes the somatic mutational landscape of normal esophagus - 0.25mm punches targeted

  Ageing epithelial tissues, including the esophagus, are progressively colonized by somatic mutant clones, some carrying mutations linked to cancer. However, whether normal epithelial mutational landscapes that evolve over decades can be altered by short term interventions such as cancer treatment is not known. Here we sequenced normal esophageal epithelium removed from patients after 5-8 weeks of therapy for esophageal cancer. Some patients had no prior treatment (NT), while others had 8 weeks of 5 Fluorouracil (5-FU) containing chemotherapy (FLOT) or 4 weeks of chemotherapy and radiation directed at the tumor (CROSS). Despite the brief duration of therapy, we observed treatment specific differences in the selection of mutant clones. The median size of mutant TP53 and PPM1D clones in the CROSS group was double that in the other groups. Mutants of the tumor suppressor SPOP were selected in CROSS but not FLOT or NT patients. In FLOT patients we found positive selection of activating MTOR mutations, consistent with the hypothesis that these mutants confer 5-FU resistance. Sequencing densely mutated normal epithelia after cancer reveals treatment specific selection of mutations with potential therapeutic impact.

  Dataset EGAD00001011182

• Cancer treatment re-shapes the somatic mutational landscape of normal esophagus - nanoseq

  Ageing epithelial tissues, including the esophagus, are progressively colonized by somatic mutant clones, some carrying mutations linked to cancer. However, whether normal epithelial mutational landscapes that evolve over decades can be altered by short term interventions such as cancer treatment is not known. Here we sequenced normal esophageal epithelium removed from patients after 5-8 weeks of therapy for esophageal cancer. Some patients had no prior treatment (NT), while others had 8 weeks of 5 Fluorouracil (5-FU) containing chemotherapy (FLOT) or 4 weeks of chemotherapy and radiation directed at the tumor (CROSS). Despite the brief duration of therapy, we observed treatment specific differences in the selection of mutant clones. The median size of mutant TP53 and PPM1D clones in the CROSS group was double that in the other groups. Mutants of the tumor suppressor SPOP were selected in CROSS but not FLOT or NT patients. In FLOT patients we found positive selection of activating MTOR mutations, consistent with the hypothesis that these mutants confer 5-FU resistance. Sequencing densely mutated normal epithelia after cancer reveals treatment specific selection of mutations with potential therapeutic impact.

  Dataset EGAD00001011183

• Cancer treatment re-shapes the somatic mutational landscape of normal esophagus - 2mm targeted

  Ageing epithelial tissues, including the esophagus, are progressively colonized by somatic mutant clones, some carrying mutations linked to cancer. However, whether normal epithelial mutational landscapes that evolve over decades can be altered by short term interventions such as cancer treatment is not known. Here we sequenced normal esophageal epithelium removed from patients after 5-8 weeks of therapy for esophageal cancer. Some patients had no prior treatment (NT), while others had 8 weeks of 5 Fluorouracil (5-FU) containing chemotherapy (FLOT) or 4 weeks of chemotherapy and radiation directed at the tumor (CROSS). Despite the brief duration of therapy, we observed treatment specific differences in the selection of mutant clones. The median size of mutant TP53 and PPM1D clones in the CROSS group was double that in the other groups. Mutants of the tumor suppressor SPOP were selected in CROSS but not FLOT or NT patients. In FLOT patients we found positive selection of activating MTOR mutations, consistent with the hypothesis that these mutants confer 5-FU resistance. Sequencing densely mutated normal epithelia after cancer reveals treatment specific selection of mutations with potential therapeutic impact.

  Dataset EGAD00001011184

• HiSeq Colorectal Cancer Dataset

  Set of 19 patients afflicted with colorectal cancer with matching preoperative and postoperative blood plasma, PBMC, and tumor biopsy sequencing data. Originally referenced by Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med. 2020, Zviran et. al.

  Dataset EGAD00001011186

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_001 for Triple negative breast cancer sample SA609X5XB03230

  Dataset EGAD00001011212

• Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma - RNA-Seq

  This study aims to investigate the dysregulation of RNA translation and identify functional non-canonical open reading frames (ORFs) as potential targets for medulloblastoma treatment. The study involves ribosome profiling and RNAseq of medulloblastoma tissues and cell lines to observe the translation of non-canonical ORFs. Multiple CRISPR-Cas9 screens will be used to identify functional non-canonical ORFs implicated in medulloblastoma cell survival.

  Dataset EGAD00001011192

• Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma - Ribo-Seq

  This study aims to investigate the dysregulation of RNA translation and identify functional non-canonical open reading frames (ORFs) as potential targets for medulloblastoma treatment. The study involves ribosome profiling and RNAseq of medulloblastoma tissues and cell lines to observe the translation of non-canonical ORFs. Multiple CRISPR-Cas9 screens will be used to identify functional non-canonical ORFs implicated in medulloblastoma cell survival.

  Dataset EGAD00001011193

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_002 for Triple negative breast cancer sample SA609X5XB03231

  Dataset EGAD00001011213

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_002 for Triple negative breast cancer sample SA609X7XB03510

  Dataset EGAD00001011218

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_001 for Triple negative breast cancer patient-derived xenograft SA609X7XB03505

  Dataset EGAD00001011219

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0063_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0063_000 for Triple negative breast cancer patient-derived xenograft SA609X3XB01584

  Dataset EGAD00001011209

• 10x Single Cell Gene Expression Library TENX068

  10x Single Cell Gene Expression library TENX068 for Triple negative breast cancer patient-derived xenograft SA609X4XB03080

  Dataset EGAD00001011210

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_002 for Triple negative breast cancer patient-derived xenograft SA609X5XB03223

  Dataset EGAD00001011214

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_001 for Triple negative breast cancer patient-derived xenograft SA609X6XB03401

  Dataset EGAD00001011215

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_002 for Triple negative breast cancer sample SA609X6XB03404

  Dataset EGAD00001011216

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_001 for Triple negative breast cancer sample SA609X6XB03447

  Dataset EGAD00001011217

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_002 for Triple negative breast cancer sample SA609X7XB03554

  Dataset EGAD00001011220

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0148_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0148_001 for Triple negative breast cancer patient-derived xenograft SA535X4XB02498

  Dataset EGAD00001011221