7790 results for "canc*"

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• IMC-F106C-101 dose escalation

  Brenetafusp is an ImmTAC bispecific (PRAME×CD3) targeting the cancer testis antigen PRAME. In this phase 1 study (NCT04262466), 126 HLA-A*02:01+ patients with advanced solid tumors received weekly IV doses from 0.3-320 mcg. The primary objective was to identify the maximum tolerated dose (MTD) or recommended phase 2 dose (RP2D); additional objectives included pharmacokinetics, pharmacodynamics, and preliminary anti-tumor activity. Brenetafusp demonstrated manageable safety and preliminary efficacy in PRAME+ tumors. A phase 3 trial in metastatic cutaneous melanoma is ongoing

  Study EGAS50000001976

• RNA-seq for "A preinvasive regulatory T cell axis for lung cancer interception" paper

  Bulk RNA sequencing data generated from primary resected non-small cell lung cancer (NSCLC) tumour tissue collected through the ASCENT study (Analysis of Screen-Detected Lung Cancers’ Genomic Traits; NCT04204499). The dataset comprises tumour RNA-seq from 89 patients with CT screen-detected NSCLC, predominantly early-stage disease, including lung adenocarcinoma and lung squamous cell carcinoma. The RNA-seq data were used to characterise tumour-immune transcriptional programmes and were analysed alongside matched peripheral blood immune profiling and clinical follow-up data.

  Dataset EGAD50000002852

• LUAD RNAseq – LORD

  RNA-seq data from paired normal and adenocarcinoma lung samples obtained from 515 patients that underwent lung cancer surgery.

  Study EGAS50000001705

• LUAD RNAseq - LORD

  The Lung Oncology Research and Discovery (LORD) program is a multi-omics translational initiative aimed at advancing precision medicine in lung cancer. It builds on a lung tissue biobank and leverages biological samples collected from patients who underwent lung cancer surgery at the IUCPQ-UL (Quebec City, Canada). LORD is therefore a single-site, retrospective, case-only study. Genomic profiling of biobanked samples is ongoing, with samples being analyzed sequentially in phases defined by sample type and omic modality. For this dataset, we provide RNA-seq data from paired normal and adenocarcinoma lung samples obtained from 515 patients.

  Dataset EGAD50000002450

• Aggressive PDACs show hypomethylation of repetitive elements and the execution of an intrinsic IFN program linked to a ductal cell-of-origin

  Study EGAS00001004660

• Somatic_mutational_landscape_in_individuals_with_germline_cancer_predisposition_WGS

  This work aims to study the somatic impact, in normal and malignant tissues, of varying germline cancer predisposition syndromes.

  Study EGAS00001007871

• Sequencing data for oesophageal and related samples - Katz-Summercorn, Jammula et al (WGS)

  Data supporting: “Multi-omic cross-sectional cohort study of pre-malignant Barrett’s esophagus reveals structural variation and retrotransposon activity occur early in cancer evolution.” Katz-Summercorn, Jammula et al. WGS (BAM files)

  Dataset EGAD00001006349

• Somatic mutational landscape in individuals with germline cancer predisposition

  WGS data from normal tissues of individuals with known or suspected germline cancer predisposition

  Dataset EGAD00001016351

• IMMUcan SYNERGY BC1 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with advanced or metastatic triple-negative breast cancer (TNBC) treated with first-line chemo-immunotherapy trial of durvalumab, paclitaxel, and carboplatin with or without the anti-CD73 antibody oleclumab enrolled in the SYNERGY trial. Comprehensive profiling data are integrated with longitudinally collected clinical information. Cellular profiling of the same tumor samples was performed using multiplex immunofluorescence imaging, imaging mass cytometry, and H&E staining.

  Study EGAS50000002010

• MOSAIC Window Glioblastoma Data

  This dataset will include Spatial Transcriptomics, Single-Cell RNA-Seq, Bulk RNA-Seq, Clinical data, WES, and H&E data from 10 Glioblastoma (GBM) Cancer patients. Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

  Dataset EGAD50000001352

• Multi-omic and functional analysis for classification and treatment of sarcomas with FUS-TFCP2 or EWSR1-TFCP2 fusions(H021/INF)

  Many rare cancers are not well understood, and pathogenesis-directed therapies are often lacking, resulting in poor patient outcomes. Leveraging two (inter)national precision oncology trials that enroll large numbers of rare cancers, we investigated the clinical, histopathologic, molecular, and functional characteristics of rhabdomyosarcoma (RMS) with fusions of FUS or EWSR1 to the TFCP2 transcription factor, a recently discovered ultra-rare entity whose classification, pathogenesis, and optimal treatment are unclear. Unusually for fusion-driven sarcomas, most cases had highly rearranged genomes, including chromothripsis, and signs of defective homologous recombination DNA repair. All tumors showed extreme expression of a truncated TERT variant and the ALK receptor tyrosine kinase, which was additionally affected by intragenic deletions and aberrant splicing, resulting in the expression of shortened variants in 58% of cases. Three ALK variants were oncogenic in immortalized cells, and patient-derived tumor cells expressing two variants responded to certain ALK inhibitors. Other recurrent alterations included CDKN2A/MTAP co-deletions and mutations in PAPPA2, encoding an IGFBP5-specific proteinase, in 67% and 25% of cases, respectively. DNA methylation analysis, along with 19 other soft-tissue sarcoma classes, revealed a close relationship with undifferentiated sarcoma but not other RMS subtypes, suggesting that TFCP2-rearranged RMS is a separate entity, possibly arising from a distinct cell of origin. Expression of TFCP2 fusions in immortalized human cells blocked late myogenic differentiation and significantly induced genes that were also highly expressed in patient tumors, including ALK, TERT, and two known regulators of skeletal muscle cells, IGFBP5 and PTH1R. Genome-wide chromatin profiling demonstrated direct binding of FUS-TFCP2 to the ALK and TERT loci outside their regular promoters, which correlated with the expression of alternative transcript variants. Finally, FUS-TFCP2 inhibited the repair of DNA double-strand breaks in immortalized cells, rendering them sensitive to treatment with cisplatin. This study provides insight into the pathogenesis and therapeutic vulnerabilities of a new RMS subtype and illustrates the value of linking comprehensive molecular diagnostics and functional annotation within multicenter consortia to improve the understanding and clinical management of rare cancers.

  Study EGAS00001006939

• Towards Objective Anal Cancer Screening: From Swab-Based Genome-Wide Methylation Marker Discovery to Novel Test Development

  Anal high-grade squamous intraepithelial lesions (HSIL-AIN2/3) are precursors to anal cancer, and early detection and treatment through screening is crucial for effective cancer prevention. Current guidelines recommend anal swab-based screening and highlight the need for objective biomarkers to improve risk stratification. While DNA methylation markers have shown strong potential in anal biopsies, their performance in anal swabs is reduced, likely due to sample-type dependency. This study aimed to identify a DNA methylation classifier specifically derived from anal swabs of men who have sex with men living with HIV (MSMLWH) to enhance diagnostic accuracy. Genome-wide methylation profiling was performed on anal swabs from 36 HSIL-AIN3 cases, 34 controls (<low-grade squamous intraepithelial lesions (<LSIL)), and 7 anal cancer patients using the 850K Infinium MethylationEPIC BeadChip (Illumina). Candidate CpG sites were selected via logistic regression with adaptive multi-group ridge penalties using co-data and an empirical Bayes approach (ecpc). The top candidate markers were validated using quantitative methylation-specific PCR (qMSP) on an independent series of 204 swabs to identify an optimal methylation classifier. Ecpc analysis identified 50 CpG sites with a combined area-under-the-curve (AUC) of 0.80. All ten candidate markers evaluated by qMSP showed significant increases in methylation levels associated with disease severity (p<0.001). From these, a 3-gene methylation classifier was identified, demonstrating excellent performance with an AUC of 0.80 (95% confidence interval:0.72-0.88) in distinguishing HSIL-AIN3+ from ≤LSIL. This study identified a robust 3-gene methylation classifier with strong potential for detecting HSIL-AIN3 and anal cancer when applied to anal swabs from MSMLWH.

  Study EGAS00001008433

• The Natural History of Human Prostate Cancer

  Whole genome DNA sequencing was used to decrypt the phylogeny of multiple samples from distinct areas of cancer and morphologically normal tissue taken from the prostates of 3 men. For each of three different prostates, multiple tumour samples (4, 5, and 3 depending on the case) and one normal tissue sample were whole genome sequenced with a matched blood sample using the Illumiuna HiSeq platform. Tumour samples were sequenced to a target depth of 50X and normals and blood to a target depth of 30X. As of September 2020, some of the studies using these data include: Cooper et al, Nature Genetics 2015 (PMID: 25730763) Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001000689

• Batches 1-3 prostatectomy analysis

  Whole Genome Sequencing Illumina HiSeq data from 95 men with prostate cancer. Samples were taken from primary tissue obtained at prostatectomy (target sequencing depth 50X) with matched blood control (target sequencing depth 30X). This data is from batches 1 to 3 and is the bulk of the data used in Wedge et al, Nature Genetics 2018 (PMID: 29662167). As of September 2020, some of the studies using these data include: Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001001116

• Non-invasive anal (pre)cancer dataset

  Molecular analysis of DNA from human anal swabs offers a non-invasive method for anal cancer screening. Current guidelines recommend anal swab-based screening and highlight the need for objective biomarkers to improve risk stratification. We performed genome-wide methylation profiling on DNA from 86 anal swab samples, including 40 HSIL-AIN3 cases (high-grade squamous intraepithelial lesions), 39 controls (<=LSIL), and 7 anal cancer patients, using the 850K Infinium MethylationEPIC BeadChip (Illumina). From this analysis, we identified a three-gene methylation classifier that showed strong discriminatory performance in distinguishing HSIL-AIN3 and anal cancer from <=LSIL lesions and successfully identified all anal cancer cases when applied to an independent validation cohort.

  Dataset EGAD00010002832

• Paediatric MN1::ZNF341-associated cancer scRNA-seq

  A lethal round-cell malignancy with an MN1::ZNF341 fusion has recently been reported. Here, using single-cell RNA sequencing data derived from monozygotic twins, we find the tumour to be transcriptionally distinct from neuroblastoma. Instead, it exhibits steroidogenic differentiation. These data, in combination with the clinical, radiological, histopathological and additional molecular findings, corroborate suggestions that this tumour is a distinct and aggressive childhood cancer.

  Dataset EGAD00001016421

• ICGC Prostate UK RNASeq BAMs

  This dataset contains aligned RNA-seq BAM files generated from prostate cancer and morphologically normal tissue samples from the ICGC Prostate UK cohort. Libraries were prepared using the Illumina TruSeq Stranded mRNA Library Preparation Kit with poly(A) selection and sequenced on the Illumina HiSeq 4000 platform. Sequencing reads (150 bp) were aligned to the reference genome using STAR.

  Dataset EGAD00001016433

• ICGC Prostate UK EPICSeq methylation sequencing data BAMs

  This dataset contains aligned BAM files generated from DNA methylation sequencing of prostate cancer and morphologically normal tissue samples from the ICGC Prostate UK cohort. Libraries were prepared using the Illumina TruSeq Methyl Capture EPIC (EPIC-Seq) assay and sequenced on the Illumina HiSeq 4000 platform. Sequence reads were aligned to the reference genome using Bismark v0.22.1.

  Dataset EGAD00001016579

• HSC_population_dynamics_KSP_samples

  My research project aims to use the clonal dynamics of spontaneously occurring somatic mutations to answer fundamental questions about human haematopoietic stem cell (HSC) biology. The four major questions I will address are: 1. How do age and aging affect normal human HSC dynamics in vivo? 2. How do in vivo perturbations, particularly chemotherapy and increased levels of reactive oxygen species, affect HSC population dynamics? 3. Is response to in vitro perturbation heritable and/or correlated with other features such as age of individual and contribution of the lineage to peripheral blood? 4. How are HSC dynamics altered in people with early driver mutations (clonal haematopoiesis)?

  Study EGAS00001002762

• Immune Biomarkers in Metastatic Castration-resistant Prostate Cancer

  Background: Metastatic castration-resistant prostate cancer (mCRPC) is a heterogeneous disease in which molecular stratification is needed to improve clinical outcomes. The identification of predictive biomarkers can have a major impact on the care of these patients, but the availability of metastatic tissue samples for research in this setting is limited. Objective: To study the prevalence of immune biomarkers of potential clinical utility to immunotherapy in mCRPC by tumour RNA sequencing, and to determine their association with overall survival (OS).

  Study EGAS50000001269

• Myeloproliferative_Disease_Whole_Genomes

  Wholegenome libraries will be prepared from at least two serial samples reflecting different stages of disease progression and matched constitutional DNA for 30 Myeloproliferative Disease samples. Five lanes of Illumina HiSeq sequencing will be performed on each of the tumour samples and four lanes for each of the constitutional DNA. Sequencing data will mapped to build 37 of the human reference genome and analysis will be performed to characterize the spectrum of somatic variation present in these samples including single base pair mutations, insertions, deletions as well as larger structural variants and genomic rearrangements.

  Study EGAS00001000290

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__Oesophageal_adenocarcinoma

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001003702

• The_Causes_of_Clonal_Blood_Cell_Disorders_Study___SCOR

  We will take a bone marrow aspirate and peripheral blood samples from a healthy patient aged around 60, and use flow cytometry to isolate 100 HSCs, 50 MEPs, and 50 GMPs. We will grow these up into colonies, then whole genome sequence each colony. Somatic mutations will act as a unique barcode for each clone. We will then design a panel for targeted resequencing of the mutations that we find. It will then be possible to look for these mutations in the peripheral blood over several years, to see the dynamics of how HSCs contribute to the peripheral blood in health.

  Study EGAS00001002132

• Elevated circulating tumor cells reflect high proliferation and genomic complexity in multiple myeloma - WGS validation cohort

  This reposiroty contains bulk WGS data generated by Blood Cancer Research Group (Ostrava, Czech Republic) as a part of publication named "Elevated circulating tumor cells reflect high proliferation and genomic complexity in multiple myeloma" published in Hemapshere.

  Study EGAS50000001211

• Elevated circulating tumor cells reflect high proliferation and genomic complexity in multiple myeloma - RNA validation cohort

  This reposiroty contains bulk RNA data generated by Blood Cancer Research Group (Ostrava, Czech Republic) as a part of publication named "Elevated circulating tumor cells reflect high proliferation and genomic complexity in multiple myeloma" published in Hemapshere.

  Study EGAS50000001212

• Elevated circulating tumor cells reflect high proliferation and genomic complexity in multiple myeloma - WGS

  This reposiroty contains bulk WGS data generated by Blood Cancer Research Group (Ostrava, Czech Republic) as a part of publication named "Elevated circulating tumor cells reflect high proliferation and genomic complexity in multiple myeloma" published in Hemapshere. The dataset contains 11 tumor-normal sample pairs.

  Dataset EGAD50000001724

• Elevated circulating tumor cells reflect high proliferation and genomic complexity in multiple myeloma - RNA

  This reposiroty contains bulk RNA data generated by Blood Cancer Research Group (Ostrava, Czech Republic) as a part of publication named "Elevated circulating tumor cells reflect high proliferation and genomic complexity in multiple myeloma" published in Hemapshere. The dataset contains RNAseq samples from bone marrows.

  Dataset EGAD50000001725

• Clinical Implications of Genomic Alterations in the Tumour and Circulation of Pancreatic Cancer Patients

  Clinical Implications of Genomic Alterations in the Tumour and Circulation of Pancreatic Cancer Patients

  Dataset EGAD00001001421

• Integrated Genomic, Epigenetic, and Expression Analyses of Ovarian Cancer Cell Lines

  In this dataset there are 55 whole genome sequencing samples of epithelial ovarian carcinoma (bam files).

  Dataset EGAD00001004532

• Combined MEK and BCL-2/XL inhibition is effective in high-grade serous ovarian cancer patient-derived xenograft models and BIM levels are predictive of responsiveness

  Most patients with late stage high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy but inevitably relapse and develop resistance, highlighting the need for novel therapies to improve patient outcomes. The MEK/ERK pathway is activated in a large subset of HGSOC, thus making it an attractive therapeutic target. Here, we systematically evaluated the extent of MEK/ERK pathway activation and efficacy of pathway inhibition in a large panel of well-annotated HGSOC patient-derived xenograft (PDX) models. The vast majority of models were nonresponsive to the MEK inhibitor cobimetinib (GDC-0973) despite effective pathway inhibition. Proteomic analyses of adaptive responses to GDC-0973 revealed that GDC-0973 upregulated the pro-apoptotic protein BIM, thus priming the cells for apoptosis regulated by BCL2-family proteins. Indeed, combination of both MEK inhibitor and dual BCL-2/XL inhibitor (ABT-263) significantly reduced cell number, increased cell death and displayed synergy in vitro in most models. In vivo, the GDC-0973 and ABT-263 combination was well tolerated and resulted in greater tumor growth inhibition than single agents. Detailed proteomic and correlation analyses identified two subsets of responsive models – those with high BIM at baseline that was increased with MEK inhibition and those with low basal Bim and high pERK levels. Models with low BIM and low pERK were non-responsive. Our findings demonstrate that combined MEK and BCL-2/XL inhibition has therapeutic activity in HGSOC models and provide a mechanistic rationale for clinical evaluation of this drug combination as well as the assessment of the extent to which BIM and/or pERK levels predict drug combination effectiveness in chemoresistant HGSOC.

  Dataset EGAD00001005111

• The dataset for Genome-wide cell-free DNA fragmentation in patients with cancer

  The dataset for Genome-wide cell-free DNA fragmentation in patients with cancer includes 538 bam files from whole genome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include plasma samples from healthy individuals and patients with cancer.

  Dataset EGAD00001005339

• Dataset for white blood cell and cell-free DNA analyses for detection of residual disease in gastric cancew

  The dataset for white blood cell and cell-free DNA analyses for detection of residual disease in gastric cancer includes 169 bam files from targeted deep sequencing on the Illumina HiSeq2500. The samples analyzed include genomic DNA from white blood cells and cell-free DNA from longitudinal blood collections of patients with gastric cancer.

  Dataset EGAD00001005750

• The dataset for Detection and characterization of lung cancer using cell-free DNA fragmentomes

  The dataset for Detection and characterization of lung cancer using cell-free DNA fragmentomes includes 872 bam files from whole genome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include plasma samples from healthy individuals and patients with cancer.

  Dataset EGAD00001007796

• The dataset for Single molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

  The dataset for Single molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer includes 57 BAM files from whole genome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include plasma samples from individuals with and without cancer.

  Dataset EGAD00001010891

• The dataset for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes

  The dataset for Detecting Liver Cancer Using Cell-Free DNA Fragmentomes includes 444 BAM files from whole genome next-generation sequencing on the Illumina NovaSeq 6000. The samples analyzed include plasma samples from individuals with and without cancer.

  Dataset EGAD00001010931

• Genome and transcriptome sequence data from a unknown – likely pancreatobiliary / intrahepatic cholangiocarcinoma in liver patient

  Genome and transcriptome sequence data from a unknown – likely pancreatobiliary / intrahepatic cholangiocarcinoma in liver patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001010989

• The dataset for "Validation of cfDNA fragmentome analyses for early detection of liver cancer"

  The dataset for “Validation of cfDNA fragmentome analyses for early detection of liver cancer” includes 380 cram files from whole genome next-generation sequencing on the Illumina NovaSeq 6000 with 10x coverage. The samples analyzed include plasma samples from patients with cancer and liver disease.

  Dataset EGAD00001015538

• Resolution of tumour cell populations enhances specificity of treatment options for precision cancer medicine

  This dataset comprises FASTQ files for single-cell RNA-seq and single-cell ATAC-seq data. These data were generated using 10x Genomics' multiome kit. In this cohort, there are 25 samples from 17 human cancer patients.

  Dataset EGAD00001015782

• Genome and transcriptome sequence data from a invasive metastatic adenocarcinoma, originating from extramammary paget’s disease patient

  Genome and transcriptome sequence data from a invasive metastatic adenocarcinoma, originating from extramammary paget’s disease patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001015940

• Genome and transcriptome sequence data from a primary unknown – possibly lung patient

  Genome and transcriptome sequence data from a primary unknown – possibly lung patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001015950

• Genome and transcriptome sequence data from a primary unknown –looks like lung with nodes and pleural disease. prior breast cancer patient

  Genome and transcriptome sequence data from a primary unknown –looks like lung with nodes and pleural disease. prior breast cancer patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001015970

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

  Genome and transcriptome sequence data from a non-small cell lung cancer patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016178

• Genome and transcriptome sequence data from a metastatic breast carcinoma patient

  Genome and transcriptome sequence data from a metastatic breast carcinoma patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016179

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016180

• Genome and transcriptome sequence data from a metastatic breast carcinoma patient

  Genome and transcriptome sequence data from a metastatic breast carcinoma patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016181

• Genome and transcriptome sequence data from a metastatic clear cell ovarian cancer patient

  Genome and transcriptome sequence data from a metastatic clear cell ovarian cancer patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016182

• Genome and transcriptome sequence data from a metastatic colorectal carcinoma patient

  Genome and transcriptome sequence data from a metastatic colorectal carcinoma patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016183

• Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient

  Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016186

• Genome and transcriptome sequence data from a locally advanced breast cancer patient

  Genome and transcriptome sequence data from a locally advanced breast cancer patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016187

• Genome and transcriptome sequence data from a metastatic rectal cancer patient

  Genome and transcriptome sequence data from a metastatic rectal cancer patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001016188