7344 results for "canc*"

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• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the stomach patient

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

  Dataset EGAD00001004652

• Genome and transcriptome sequence data from a metastatic melanoma patient

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

  Dataset EGAD00001004653

• Genome and transcriptome sequence data from a breast invasive ductal carcinoma patient

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

  Dataset EGAD00001004654

• Genome and transcriptome sequence data from a locally advanced oropharyngeal carcinoma patient

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

  Dataset EGAD00001004655

• Genome and transcriptome sequence data from a primary unknown patient

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

  Dataset EGAD00001004656

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

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

  Dataset EGAD00001004657

• Genome and transcriptome sequence data from a metastatic osteosarcoma patient

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

  Dataset EGAD00001004658

• 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 EGAD00001004659

• Genome and transcriptome sequence data from a metastatic primitive neuro-ectodermal tumor of the testicle patient

  Genome and transcriptome sequence data from a metastatic primitive neuro-ectodermal tumor of the testicle patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004660

• Genome and transcriptome sequence data from a metastatic clear cell carcinoma of the ovary patient

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

  Dataset EGAD00001004661

• Genome and transcriptome sequence data from a metastatic leiomyosarcoma of pancreas patient

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

  Dataset EGAD00001004662

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the pancreas patient

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

  Dataset EGAD00001004663

• Genome and transcriptome sequence data from a neuroendocrine carcinoma patient

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

  Dataset EGAD00001004664

• 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 EGAD00001004672

• Somatic pseudogenes acquired during cancer development – Whole Exome sequencing

  Insertion of processed pseudogenes is known to occur in the germline but has not previously been observed in somatic cells. Formation of pseudogenes could represent a new class of mutation in cancers and a new source of potential driver events.

  Dataset EGAD00001000637

• Somatic pseudogenes acquired during cancer development – Whole Genome sequencing

  Insertion of processed pseudogenes is known to occur in the germline but has not previously been observed in somatic cells. Formation of pseudogenes could represent a new class of mutation in cancers and a new source of potential driver events.

  Dataset EGAD00001000638

• Somatic pseudogenes acquired during cancer development – RNAseq

  Insertion of processed pseudogenes is known to occur in the germline but has not previously been observed in somatic cells. Formation of pseudogenes could represent a new class of mutation in cancers and a new source of potential driver events.

  Dataset EGAD00001000639

• Chordoma Sequencing Project RNAseq

  This is a continuation of the Chordoma Sequencing Project. All cancers arise due to somatically acquired abnormalities in DNA sequence. Systematic sequencing of cancer genomes allows acquisition of complete catalogues of all classes of somatic mutation present in cancer. These mutation catalogues will allow identification of the somatically mutated cancer genes that are operative and characterise patterns of somatic mutation that may reflect previous exogenous and endogenous mutagenic exposures. In this application, we aim to perform whole genome sequencing on 10 chordoma matched genome pairs. RNA Sequencing/Methylation and SNP6 and an additional sequencing of three cancer cell lines will be added to this work.

  Dataset EGAD00001000653

• RNAseq of patients with Ewings sarcoma

  Cancer is driven my mutations in the genome. We will uncover the mutations that give rise to Ewing's sarcoma, a bone tumour that largely affects children. We will use second generation Illumina massively parallel sequencing, and bespoke software, to characterise the genomes and transcriptomes of Ewing's sarcoma tumours.

  Dataset EGAD00001000444

• Targeted sequencing of genes recurrently mutated in AML

  Background Massively parallel sequencing technology has transformed cancer genomics. It is now feasible, in a clinically relevant time-frame, for a clinically manageable cost, to screen DNA from patient tumours for mutations essentially genome-wide. The challenge for personalised medicine will be to increase the sample size to thousands or tens of thousands of well-characterised cases in order to attain sufficient statistical power to stratify patients accurately across the complexity and genomic heterogeneity expected for most of the common tumour types. Currently, whole genome sequencing on this scale is not feasible, and targeted sequencing of relevant portions of the genome will be required. Pilot data We have developed protocols for large-scale, multiplexed sequencing of 100-200 genes in thousands of samples. Essentially, using robotic technology, genomic DNA from the cancer specimen is processed into sequencing libraries with unique DNA barcodes, thereby allowing sequencing reads to be attributed to the sample they derive from. Currently, these sequencing libraries can be generated in a 96-well format using fully automated protocols, and we are exploring methods to expand this to a 384-well format. The sequencing libraries are pooled and hybridized to custom sets of RNA baits representing the genomic regions of interest. Sequencing of the pulled-down libraries is done in pools of 48-96 samples per lane of an Illumina Hi-Seq. This protocol is already implemented at the Sanger Institute. We have published proof that somatic mutations in novel cancer genes can be identified from exome-wide sequencing. In unpublished pilot data, we have established the feasibility of robotic library production, custom pull-down, and multiplexed sequencing of barcoded libraries for 100 known myeloid cancer genes across 760 myelodysplasia samples. Highlights of the data thus far analysed reveal that the coverage is remarkably even between samples; when 96 samples are run, average coverage per lane of sequencing is ~250, with 90-95% of targeted exons covered by >25 reads; known mutations can be discovered in the data set; and the protocol is amenable to whole genome amplified DNA. The bioinformatic algorithms for identification of substitutions and indels in pull-down data are well-established; we have pilot data proving that copy number changes, LOH and genomic rearrangements in specific regions of interest can also be identified by tiling of baits across the relevant loci. Proposal We propose to apply this methodology to 10000 samples from patients with AML enrolled in clinical trials over the last 10-20 years. Oncogenic point mutations and potentially genomic rearrangements will be identified, and linked to clinical outcome data, with a view to undertaking the following sorts of analyses: ? Identification of co-occurrence, mutual exclusivity and clusters of driver mutations. ? Correlation of prognosis with driver mutations and potentially gene-gene interactions ? Exploration of genomic markers of drug response Ultimately, we would like to be in a position to release the mutation data together with matched clinical outcome data to genuine medical researchers via a controlled access approach, possibly within the COSMIC framework (www.sanger.ac.uk/genetics/CGP/cosmic/). The vision here is to generate a portal whereby a clinician faced with an AML patient and his / her mutational profile can obtain a ?personalised? prediction of outcome, together with a fair assessment of the uncertainty of the estimate. With a sufficient sample size, there would also be the potential to develop decision support algorithms for therapeutic choices based on such data.

  Dataset EGAD00001000606

• Whole transcriptome sequencing of untreated and castration resistant prostate cancer

  Whole transcriptome sequencing of 28 untreated prostate cancers, 13 castration resistant prostate cancers, and 12 benign prostatic hyperplasias.

  Dataset EGAD00001000609

• Small RNA sequencing of untreated and castration resistant prostate cancer

  Small RNA sequencing of 28 untreated prostate cancers, 12 castration resistant prostate cancers, and 3 benign prostatic hyperplasias.

  Dataset EGAD00001000611

• Whole genome sequencing of untreated and castration resistant prostate cancers

  Low coverage whole genome sequencing of 27 untreated prostate cancers, 9 castration resistant prostate cancers, and 4 benign prostatic hyperplasias.

  Dataset EGAD00001000612

• Prostate Cancer Whole Genome Validations

  We propose to definitively characterise the somatic genetics of Prostate cancer through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing. This study will aim to validate the findings of the whole genome study by re-sequencing regions of interest using a bespoke pulldown bait. See ICGC website for more information: http://icgc.org/icgc/cgp/70/508/71331

  Dataset EGAD00001000621

• Characterization of individual foci of multicentric/multifocal breast cancer using targeted next generation sequencing

  Multifocality or multicentricity in breast cancer may be defined as the presence of two or more tumor foci within a single quadrant of the breast or within different quadrants of the same breast, respectively. This original classification of the breast cancer as multicentric or multifocal was based on the assumption that cancers arising in the same quadrant were more likely to arise from the same ductal structures than those occurring in separate areas of the breast. The problem with these definitions is that the ?quadrants? of the breast are arbitrary external designations, as no internal boundaries do exist. This project will therefore focus both on synchronous multifocal and multicentric tumors. The incidence of multifocal and multicentric breast cancers was reported to be between 13 and 75% depending on the definition used, the extent of the pathologic sampling of the breast and whether in situ disease is considered evidence of multicentricity (1). Although this incidence is variable, those figures show that it is a frequent phenomenon. Multiple (multifocal/multicentric) breast carcinomas, especially when occurring in the same breast, represent a real challenge for both pathologists and clinicians in terms of identifying the cellular origin and the best therapeutic management of the cancer. Multifocality or multicentricity has been associated with a number of more aggressive features including an increased rate of regional lymph node metastases and adverse patient outcome when compared with unifocal tumors (2-3), and a possible increased risk of local recurrence following breast conserving surgery (4). For the moment, the literature is divided on whether there is a corresponding impact on survival outcomes. Today, the current convention to stage and to treat multifocal and multicentric tumors is the classical tumor-node-metastasis (TNM) staging guidelines with which tumor size is assessed by the largest tumor focus without taking other foci of disease into consideration. If some papers, as the recent one from Lynch and colleagues, support the current staging convention (3), others, however, as Boyages et al. suggested that aggregate size and not the size of the largest lesion should be considered in order to refine the prognostic assessment of those tumors (5). On the top of that, the question whether multifocal/multicentric carcinomas are due to the spread of a single carcinoma throughout the breast or is due to multiple carcinomas arising simultaneously has been a matter of debate. Some studies suggested that multifocal breast cancer may result from either intramammary spread from a single primary tumor or multiple synchronous primary tumors; whereas others suggest that multiple breast carcinomas always arise from the same clone (6-8). Recently, Pietri and colleagues analyzed the biological characterization of a series of 113 multifocal/multicentric breast cancers (8) which were diagnosed over a 5-year period. The expression of estrogen (ER) and progesterone (PgR) receptors, Ki-67 proliferative index, expression of HER2 and tumor grading were prospectively determined in each tumor focus, and mismatches among foci were recorded. Mismatches in ER status were present in 5 (4.4%) cases and PgR in 18 (15.9%) cases. Mismatches in tumor grading were present in 21 cases (18.6%), proliferative index (Ki-67) in 17 (15%) cases and HER2 status in 11 (9.7%) cases. Interestingly, this heterogeneity among foci has led to 14 (12.4%) patients receiving different adjuvant treatments compared with what would have been indicated if we had only taken into account the biologic status of the primary tumor. This study therefore showed that differences in biological characteristics of multifocal/multicentric lesions play a crucial role in the adjuvant treatment decision making process. In this study, we will concentrate on a larger series of patients with multifocal invasive ductal breast cancer lesions. We aim at: 1. Evaluating the incidence of multifocality according to the different breast cancer molecular subtypes (ER-/HER2-, HER2+, ER+/HER2-). 2. Evaluating the incidence of multifocality in patients with hereditary breast cancer disease (presence of germline BRCA1 or BRCA2 mutations). Moreover, we would like to investigate if multifocal lesions with BRCA1 or BRCA2 mutations exhibit a characteristic combination of substitution mutation signatures and a distinctive profile of deletions as demonstrated recently by Nik-Zainal and colleagues (9). 3. Correlating multifocality with clinical information in order to define its influence on patients? survival (DFS and OS). 4. Carrying high coverage targeted gene sequencing of driver cancer genes and genes whose mutation is of therapeutic importance in order to compare clinically-relevant genetic differences between several multifocal breast cancer lesions. 5. Evaluating the impact of the distance between the different lesions on the clinical outcome but also on the genetic differences. 6. Comparing gene expression patterns between several multifocal breast cancer lesions and correlate them with the results of the targeted genes screen. 7. Characterizing the genomic and transcriptomic status of cancer related genes in metastatic lesions (local recurrence, positive lymph node or distant metastatic sites) from the same multifocal invasive ductal breast cancer patients in order to evaluate the consequence of genomic and transcriptomic heterogeneity of multifocal lesions on metastatic lesions. Multiple (multifocal/multicentric) breast carcinomas, especially when occurring in the same breast, represent a real challenge for both pathologists and clinicians in terms of identifying the cellular origin and the best therapeutic choice. This project has the potential to identify genetic/transcriptomic differences existing between several lesions constituting multifocal breast cancers, which in the routine clinical practice are usually considered to be homogeneous among them. We foresee validating significant results in a larger series of patients and this, in turn, could have a remarkable impact on the treatment and clinical management of multifocal breast cancers. Indeed, we hope to provide some evidence whether or not each focus matters in multifocal and multicentric breast cancer to define the adequate therapeutic approach, especially in the context of targeted therapies. The work to be done at Sanger will be target gene screen pooling of 1400 samples.

  Dataset EGAD00001000624

• Matched Pair Cell Line Tumour RNAseq

  In this study we will sequence the transcriptome of Verified Matched Pair Cancer Cell line tumour samples. This will be married up to whole exome and whole genome sequencing data to establish a full catalog of the variations and mutations found.

  Dataset EGAD00001000630

• Constitutional and somatic genomic rearrangements coherently restructure chromosome 21 in acute lymphoblastic leukaemia

  Changes in gene dosage are a major driver of cancer1, engineered from a finite, but increasingly well annotated, repertoire of mutational mechanisms2-6. These processes operate over levels ranging from individual exons to whole chromosomes, often generating correlated copy number alterations across hundreds of linked genes. An example of the latter is the 2% of childhood acute lymphoblastic leukemia (ALL) characterized by recurrent intrachromosomal amplification of megabase regions of chromosome 21 (iAMP21)7,8 To dissect the interplay between mutational processes and selection on this scale, we used genomic, cytogenetic and transcriptional analysis, coupled with novel bioinformatic approaches, to reconstruct the evolution of iAMP21 ALL. We find that individuals born with the rare constitutional Robertsonian translocation between chromosomes 15 and 21, rob(15;21)(q10;q10)c, have ~2700-fold increased risk of developing iAMP21 ALL compared to the general population. In such cases, amplification is initiated by chromothripsis involving both sister chromatids of the dicentric Robertsonian chromosome. In contrast, sporadic iAMP21 is typically initiated by breakage-fusion-bridge (BFB) events, often followed by chromothripsis or other rearrangements. In both sporadic and iAMP21 in rob(15;21)c individuals, the final stages of amplification frequently involve large-scale duplications of the abnormal chromosome. The end-product is a derivative chromosome 21 or a derivative originating from the rob(15;21)c chromosome, der(15;21), respectively, with gene dosage optimised for leukemic potential, showing constrained copy number levels over multiple linked genes. In summary, the constitutional translocation, rob(15;21)c, predisposes to leukemia through a novel mechanism, namely a propensity to undergo chromothripsis, likely related to its dicentric nature. More generally, our data illustrate that several cancer-specific mutational processes, applied sequentially, can co-ordinate to fashion copy number profiles over large genomic scales, incrementally refining the fitness benefits of aggregated gene dosage changes.

  Dataset EGAD00001000658

• Hepatocellular adenomas samples

  Whole exome sequencing data from 30 donors (46 tumors and 30 non-tumoral whole exome sequencing, paired-end, HiSeq 2000, Illumina) collected by the Inserm U674, PI Jessica Zucman-Rossi - Institut National du Cancer (INCa), PI Fabien Calvo, France.

  Dataset EGAD00001000737

• Targeted sequencing of genes recurrently mutated in AML - part2

  Genomic libraries will be generated from total genomic DNA derived from 4000 samples with Acute Myeloid Leukaemia. Libraries will be enriched for a selected panel of genes using a bespoke pulldown protocol. 64 Samples will be individually barcoded and subjected to up to one lanes of Illumina HiSeq. Paired reads will be mapped to build 37 of the human reference genome to facilitate the characterisation of known gene mutations in cancer as well as the validation of potentially novel variants identified by prior exome sequencing.

  Dataset EGAD00001000747

• dataset for BGI bladder cancer project

  dataset for BGI bladder cancer project

  Dataset EGAD00001000758

• dataset for esophageal cancer, 17 pairs for whole-genome sequencing and 71 pairs for whole-exome sequencing

  dataset for esophageal cancer, 17 pairs for whole-genome sequencing and 71 pairs for whole-exome sequencing

  Dataset EGAD00001000760

• TMD-AMKL targeted follow-up

  Genomic libraries will be generated from total genomic DNA derived from 200+ patients with childhood Transient Myeloproliferative Disorder (TMD) and or Acute Megakaryocytic Leukemia (AMKL) as well some matched constitutional samples (n < 50 ). Libraries will be enriched for a selected panel of genes using a bespoke pulldown protocol. 96 Samples will be individually barcoded and subjected to up to two lanes of Illumina HiSeq. Paired reads will be mapped to build 37 of the human reference genome to facilitate the characterisation of known gene mutations in cancer as well as the validation of potentially novel variants identified by prior exome sequencing.

  Dataset EGAD00001000783

• Integrated genomic characterization of adrenocortical carcinoma

  Adrenocortical carcinomas (ACC) are aggressive cancers originating in the cortex of the adrenal glands. Despite the overall poor prognosis, ACC outcome is heterogeneous. CTNNB1 and TP53 mutations are frequent in these tumors, but the complete spectrum of genetic changes remains undefined. Exome sequencing and SNP array analysis of 45 ACC revealed recurrent alterations in known drivers (CTNNB1, TP53, CDKN2A, RB1, MEN1) and genes not previously reported to be altered in ACC (ZNRF3, DAXX, TERT and MED12), which were validated in an independent cohort of 77 ACC. The cell-surface transmembrane E3 ubiquitin ligase ZNRF36 was the gene the most frequently altered (21%), and appears as a potential novel tumor suppressor gene related to the ß-catenin pathway.Our integrated genomic analyses led to the identification of two distinct molecular subgroups with opposite outcome. The C1A group of poor outcome ACC was characterized by numerous mutations and DNA methylation alterations, whereas the C1B group with good prognosis displayed a specific deregulation of two miRNA clusters. Thus, aggressive and indolent ACC correspond to two distinct molecular entities, driven by different oncogenic alterations.

  Dataset EGAD00001000764

• Breast Cancer Sequential Sampling Targeted Capture

  This study aims to target capture sequence regions of interest from DNA derived from breast cancer patients who received neo-adjuvant chemotherapy. All patients had multiple biopsies performed before chemotherapy. Patients who had residual disease after the course of treatment underwent a further biopsy. We aim to characterise the mutations involved.

  Dataset EGAD00001000784

• Bone Cancer - Rare Types Whole Genome

  We propose to definitively characterise the somatic genetics of a selection of rare bone cancers through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing.

  Dataset EGAD00001000785

• SCCOHT

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

  Dataset EGAD00001000791

• Small cell carcinoma of the ovary, hypercalcemic type, displays frequent inactivating germline and somatic mutations in SMARCA4

  Small cell carcinoma of the ovary of hypercalcemic type (SCCOHT) is an extremely rare, aggressive cancer affecting children and young women. We identified germline and somatic inactivating mutations in the SWI/SNF chromatin-remodeling gene SMARCA4 in 75% (9/12) of SCCOHT patients in addition to SMARCA4 protein loss in 82% (14/17) of SCCOHT tumors, but in only 0.4% (2/485) of other primary ovarian tumors. These data implicate SMARCA4 in SCCOHT oncogenesis.

  Dataset EGAD00001000794

• Genome and transcriptome sequence data from a metastatic high-grade adenocarcinoma of the fallopian tubes patient

  Genome and transcriptome sequence data from a metastatic high-grade adenocarcinoma of the fallopian tubes patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004709

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient

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

  Dataset EGAD00001004708

• 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 EGAD00001004707

• Genome and transcriptome sequence data from a adenocarcinoma of the liver patient

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

  Dataset EGAD00001004706

• Genome and transcriptome sequence data from a metastatic pancreatic adenocarcinoma patient

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

  Dataset EGAD00001004705

• 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 EGAD00001004704

• 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 EGAD00001004703

• Genome and transcriptome sequence data from a high grade serous ovarian cancer patient

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

  Dataset EGAD00001004702

• 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 EGAD00001004701

• Genome and transcriptome sequence data from a metastatic lung adenocarcinoma patient

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

  Dataset EGAD00001004700

• Genome and transcriptome sequence data from a lung adenocarcinoma patient

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

  Dataset EGAD00001004699

• 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 EGAD00001004698

• Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient

  Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004697

• Genome and transcriptome sequence data from a peripheral T-cell lymphoma patient

  Genome and transcriptome sequence data from a peripheral T-cell lymphoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004696

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

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

  Dataset EGAD00001004695

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

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

  Dataset EGAD00001004694

• Genome and transcriptome sequence data from a metastatic carcinoma to paraspinal mass with primary unknown patient

  Genome and transcriptome sequence data from a metastatic carcinoma to paraspinal mass with primary unknown patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004693

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

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

  Dataset EGAD00001004692

• Genome and transcriptome sequence data from a metastatic transverse colon cancer patient

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

  Dataset EGAD00001004691

• Genome and transcriptome sequence data from a angiosarcoma patient

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

  Dataset EGAD00001004690

• 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 EGAD00001004689

• 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 EGAD00001004688

• 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 EGAD00001004687

• 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 EGAD00001004686

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001004711

• 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 EGAD00001004710

• 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 EGAD00001004713

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

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

  Dataset EGAD00001004715

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

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

  Dataset EGAD00001004716

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

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

  Dataset EGAD00001004714

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

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

  Dataset EGAD00001004717

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

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

  Dataset EGAD00001004718

• Feasibility of targeted capture sequencing in routinely collected FFPE cancer specimens

  Testing the feasibility of genome-scale sequencing in routinely collected formalin-fixed paraffin-embedded (FFPE) cancer specimens versus matched fresh-frozen samples using targeted pulldown capture prior to Illumina sequencing.

  Dataset EGAD00001000354

• Dataset Plasma-seq

  We performed low coverage whole genome sequencing of plasma DNA from prostate cancer patients to establish copy number profiles on both a genome-wide and a gene-specific level. The data include plasma samples from prostate cacner patients (n=13), non-malignant controls (males, n=10 and females, n=9), plasma samples from pregnancies with aneuploid and euploid fetuses (n=4). Furthermore, we sequenced different tumor samples (n=6) of one patients and a serial dilution of HT29 in a background of normal DNA (n=9).

  Dataset EGAD00001000364

• Dataset Gene-Breakpoint Panel

  In this study we analysed patients with metastatic prostate cancer to scan their tumor genomes noninvasively in plasma DNA. We enriched 1.3 Mbp of seven plasma DNAs (4 CRPC cases: CRPC1-3 and CRPC5; 3 CSPC cases: CSPC1-2 and CSPC4) including exonic sequences of 55 cancer genes and 38 introns of 18 genes, where fusion breakpoints have been described using Sure Select Custom DNA Kit.

  Dataset EGAD00001000365

• Melanoma-Til Study RNAseq

  In this study, mutations present in a series of human melanomas (stage IV disease) will be determined, using autologous blood cells to obtain a reference genome. From each of the samples that are analyzed, tumour-infiltrating T lymphocytes have also been isolated. This offers a unique opportunity to determine which (fraction of) mutations in human cancer leads to epitopes that are recognized by T cells. The resulting information is likely to be of value to understand how T cell activating drugs exert their action.

  Dataset EGAD00001000325

• Osteosarcoma whole genome rearrangement screen

  Genomic libraries (500 bps) will be generated from total genomic DNA derived from Osteosarcoma cancer patients and subjected to short paired end sequencing on the llumina platform. Paired reads will be mapped to build 37 of the human reference genome to facilitate the generation of genome wide copy number information, and the identification of novel rearranged cancer genes and gene fusions.

  Dataset EGAD00001000368

• Pancreatic organoid - case samples

  This dataset contains 31 pancreatic organoid samples used in the 'organoid data of pancreatic cancers ' study

  Dataset EGAD00001004528

• Prostate cancer datasets WES

  WES: 48 samples (5 blood samples from 6 patient data): 22 Tumour cores and 26 normal/benign cores (HiSeq)

  Dataset EGAD00001004467

• Prostate cancer datasets RNA Seq

  Total RNA Seq: 15 Samples (2 patients (MF1 and MF3)) (HiSeq) and Poly A RNA Seq: 27 Samples (4 patients Normal and Tumour ) (HiSeq)

  Dataset EGAD00001004468

• Exome sequencing of a novel cervical cancer cell line

  In this work, we establish and characterized a low-passage cervix cancer cell line from a Brazilian patient with squamous cell carcinoma. The dataset contains three samples from the same patient (blood, tumor tissue and the primary cell line). The technology used was exome sequencing and the file type available is fastq files.

  Dataset EGAD00001004480

• Targeted sequencing of cell-free DNA and white blood cells from 24 men with metastatic prostate cancer

  This dataset consists of aligned DNA sequencing data in BAM file format from cell-free DNA and white blood cells from 24 men with metastatic prostate cancer. One cell-free DNA sample and one white blood cell sample is available for each patient, resulting in 48 total BAM files in this dataset. The sequencing was performed using a hybrid capture-based targeted panel of 73 prostate cancer driver genes.

  Dataset EGAD00001004486

• Longitudinal breast cancer cohort in SMC

  Whole transcriptome sequencing (WTS) of a longitudinal breast cancer (BC) cohort consisting of 146 cases (281 tumors, 109 pairs), including 52 (38%) that achieved pathologic complete responses (pCR) and 85 (62%) that harbored residual diseases at time of surgery.

  Dataset EGAD00001004487

• UIC HNSCC RNA-Seq and miRNA-Seq Data

  This dataset contains matched RNA-Seq and miRNA-Seq fastq files from 109 match samples of 34 human Papillomavirus-negative Head and Neck cancer patients, including 72 lymph nodes, 29 tumor and 8 normal samples.

  Dataset EGAD00001004489

• Recalibrated whole-exome sequencing alignment data of papillary thyroid cancer of Saudi Arabia

  The dataset consists of samples from papillary thyroid cancer patients. A total of 11 DNA samples from blood/normal and cancer tissue are subjected to whole exome sequencing using Illumina. The fastq files generated were aligned with reference genome ‘hg19’, duplicates were marked, realignment around indels and quality recalibration were performed to produce good quality variants. The recalibrated “.bam” files are included with this dataset.

  Dataset EGAD00001004490

• Microsatellite unstable colorectal cancers

  Mapped data for 10 Colon MSI cancer samples

  Dataset EGAD00001004500

• Familial Breast cancer dataset

  Illumina HiSeqXTen platform sequencing data of whole genome libraries prepared from 156 matched tumour-normal samples from 78 donors

  Dataset EGAD00001004494

• Single cell multiomics sequencing and analyses of human colorectal cancer

  Whole genome sequencing data of tumor tissues, adjacent normal tissues, and peripheral blood from CRC patients.

  Dataset EGAD00001004495

• Genomic and transcriptomic determinants of therapy resistance and immune landscape evolution during anti-EGFR treatment in colorectal cancer

  Genomic and transcriptomic data from a cohort of 35 RAS wild-type colorectal cancers. All 35 cases were DNA sequenced at baseline (BL) before treatment with single agent cetuximab. Progressive disease (PD)-biopsies were taken shortly after radiological progression and successfully exome sequenced from 24/35 cases. mRNA sequencing is available for 25 Baseline and 15 PD samples. ctDNA from 9 cases that progressed after prolonged cetuximab benefit were also deep sequenced.

  Dataset EGAD00001004501

• Ovarian Cancer Organoid Biobank - RNASeq data

  Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Experimental in vitro models that faithfully capture the hallmarks and tumor heterogeneity of OC are limited and hard to establish. We present a novel protocol that enables efficient derivation and long-term expansion of OC organoids. Utilizing this protocol, we have established 56 organoid lines from 32 patients, representing the spectrum of ovarian neoplasms, including non-malignant borderline tumors, as well as mucinous, clear-cell, endometrioid, low- and high-grade serous carcinomas. OC organoids recapitulate histological and genomic features of the pertinent lesion from which they were derived, illustrating intra- and inter-patient heterogeneity, and can be genetically modified. We show that OC organoids can be used for drug screening assays and capture different tumor subtype responses to the gold standard platinum-based chemotherapy, including acquisition of chemoresistance in recurrent disease. Finally, OC organoids can be xenografted, enabling in vivo drug sensitivity assays. Taken together, this demonstrates their potential application for research and personalized medicine.

  Dataset EGAD00001004509

• microRNA-seq of human serum from a longitudinal study of 66 women with no history of cancer

  This dataset includes microRNA sequencing data from 198 human serum samples, representing a subset of 66 women with no history of cancer who participated in the UKCTOCS study and with serum samples collected at three timepoints over a period of up to 5 years. Small RNA libraries prepared from the serum samples were sequenced with 50-bp single end reads on an Illumina HiSeq 2000 instrument. Data is provided as FASTQ files.

  Dataset EGAD00001004348

• Exome sequencing to evaluate HER2/ERBB2 mutations in cancer

  ERBB2/HER2 transmembrane and juxtamembrane domain mutations in cancer. Exome sequencing of tumor and matched blood and 2 blood samples from relatives.

  Dataset EGAD00001004351

• Whole Exome Sequencing

  The Whole Exome Sequencing dataset contains 30 whole exome sequencing files (tumor, germ line DNA) and phenotype metadata for 15 patients on the phase II clinical trial of neoadjuvant immune checkpoint blockade in high-risk resectable melanoma at MD Anderson Cancer Center (NCT02519322). Included are data from baseline samples.

  Dataset EGAD00001004352

• Molecular portraits of breast cancer diagnosed during pregnancy

  The aim of this study was to compare the mutational landscape of breast cancer diagnosed during pregnancy (BCP) and breast cancer from age/stage non-pregnant patients (controls). We present whole genome sequencing data (Illumina HiSeq X ten platform) of tumor and matched normal tissues from 35 BCP patients and 20 controls. This work provides important novel biological insights and a unique resource to study the biology of breast cancer in young women and how pregnancy could modulate tumor biology.

  Dataset EGAD00001004353

• Dataset for "Genomic landscape of oral cancers" (Illumina WGS)

  Dataset for "Genomic landscape of oral cancers" (Illumina WGS)

  Dataset EGAD00001004356

• Summary statistics for cervical cancer GWAS

  Summary statistics from GWAS meta-analysis of cervical cancer

  Dataset EGAD00001004361

• DNA sequencing for gastric cancer ascites

  Whole-exome sequencing (WES) was performed on a total of 34 PC specimens, with 15 cases having matched gDNAs extracted from blood.

  Dataset EGAD00001004364

• RNA sequencing for gastric cancer ascites

  Whole transcriptome sequencing (RNA-seq) was performed on 39 PC specimens. Among them, 21 specimens also had WES data.

  Dataset EGAD00001004365

• Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset for "Genomic landscape of oral cancers" (Illumina RNA)

  Dataset EGAD00001004366

• Organoid Derivation Project TGS: Release 1

  Targeted gene sequencing of cancer driver genes to determine the driver mutations present in newly-derived cancer organoid models

  Dataset EGAD00001004368

• Locally advanced rectal cancer samples

  Fastq files from exome sequencing of paired normal/tumor (pre and post-nCRT) samples from 7 patients with rectal tumors. All samples were sequenced on a 5500xl SOLiD sequencing platform (Thermo Fisher Scientific).

  Dataset EGAD00001004378

• Dynamics of multiple resistance mechanisms in plasma DNA during EGFR‐targeted therapies in non‐small cell lung cancer - sWGS

  Shallow whole‐genome sequencing dataset on samples from three patients who underwent histological transformation to small‐cell lung cancer. Samples included in this dataset include normal buffy coat samples, plasma samples collected at diagnosis of NSCLC as well as prior to small‐cell transformation and after SCLC transformation and progression on cisplatin and irinotecan.

  Dataset EGAD00001004379

• Infiltrative and drug-resistant slow-cycling cells support metabolic heterogeneity in glioblastoma

  Glioblastoma patient derived Fast/Slow cycling cancer stem cell RNA sequencing. Consists of 3 patient cell lines and 6 files

  Dataset EGAD00001004380