6052 results for "canc*"

in 53.07 milliseconds.

• Widespread hypertranscription in aggressive human cancers

  Data used to validate RNAmp tool.

  Dataset EGAD00001009285

• Clinical cancer panel sequencing (UCSF500) analysis of TERT promoter duplication in GBM.

  Dataset contains paired-end clinical cancer panel sequencing (UCSF500) data from 2 samples of an initial tumor and one sample of a recurrence from one GBM patient and one sample from a second GBM patient.

  Dataset EGAD00001009286

• Normal sample for patient SA300

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA300

  Dataset EGAD00001009369

• WTS and WES of Renal Cell Carcinoma Tumors From a Phase III Anti-Angiogenic Adjuvant Therapy Trial

  Archival de-identified formalin-fixed paraffin-embedded RCC tumor tissue blocks from nephrectomy or tumor biopsy were processed as per below and the same sections were used for both DNA and RNA extractions. For WES (ACE version 3; Illumina NovaSeq), samples were profiled using Personalis ACE Cancer Exome (Personalis, Inc, Menlo Park, CA) Whole-transcriptome profiles were generated by RNA-seq (Accuracy and Content Enhanced (ACE) version 3; Illumina NovaSeq) using Personalis ACE Cancer Transcriptome (Personalis, Inc, Menlo Park, CA ) Of the 615 patients in the intent-to-treat population in S-TRAC trial, 193 individual specimens were available for molecular profiling, of which 171 (27.8%) (sunitinib, n = 91; placebo, n = 80) returned results for the WES analysis, and 133 (21.6%) (sunitinib, n = 72; placebo, n = 61) returned results for the GES analysis. Of the 138 WTS samples with data, replicates for two patients were summarized by median expression, and three samples were excluded from the final analysis due to low counts.

  Dataset EGAD00001009294

• Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia - RNA

  Genomic profiling at diagnosis of B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification and treatment decisions. Patients for which diagnostic screening fails to identify disease defining or risk stratifying lesions are classified as B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) on paired tumor-normal samples. For 52 B-other patients we compared WGS findings to data from clinical and research cytogenetics. WGS identifies a cancer associated event in 51/52 cases, this includes an established subtype defining genetic alteration in 5/52 that were previously missed by standard-of-care genetics. Of the 47 true B-other ALL we identified a recurrent driver in 87% (41).  Complex karyotype by cytogenetics emerges as a heterogeneous group, underlied by distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r, IGK::BCL2). For a subset of 31 cases, we integrate findings from RNA-sequencing (RNA-seq) analysis to include fusion gene detection, and classification by gene expression. Compared to RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes, however RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrate that WGS can identify clinically relevant genetic abnormalities missed by standard-of-care testing and identify leukemia driver events in virtually all cases of B-other ALL.

  Dataset EGAD00001009305

• Normal sample for patient SA280

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA280

  Dataset EGAD00001009365

• Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia

  Genomic profiling at diagnosis of B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification and treatment decisions. Patients for which diagnostic screening fails to identify disease defining or risk stratifying lesions are classified as B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) on paired tumor-normal samples. For 52 B-other patients we compared WGS findings to data from clinical and research cytogenetics. WGS identifies a cancer associated event in 51/52 cases, this includes an established subtype defining genetic alteration in 5/52 that were previously missed by standard-of-care genetics. Of the 47 true B-other ALL we identified a recurrent driver in 87% (41).  Complex karyotype by cytogenetics emerges as a heterogeneous group, underlied by distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r, IGK::BCL2). For a subset of 31 cases, we integrate findings from RNA-sequencing (RNA-seq) analysis to include fusion gene detection, and classification by gene expression. Compared to RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes, however RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrate that WGS can identify clinically relevant genetic abnormalities missed by standard-of-care testing and identify leukemia driver events in virtually all cases of B-other ALL.

  Dataset EGAD00001009304

• WGS of cell-free DNA derived from plasma of CRC patients and healthy controls, and WGS of matched tumor tissue and saliva

  This dataset contains: 1.) Whole-genome sequencing (WGS) data (~6x) of 259 cfDNA samples obtained from 50 colorectal cancer (CRC) patients and 61 healthy controls. Paired-end sequencing was performed with 2x101 bp reads on the NovaSeq 6000 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38). 2.) WGS data (~1x) of 50 tumor biopsy and 45 saliva samples from CRC patients. Paired-end sequencing was performed with 2x101 bp reads on the NovaSeq 6000 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38).

  Dataset EGAD00001009309

• WES and RNA-seq of triple-negative breast cancers from the MyBrCa cohort

  The dataset contains a genomics characterization of 35 triple-negative Asian breast tumours from the Malaysian Breast Cancer cohort. This includes whole-exome sequencing of tumour tissue at 80X, whole-exome sequencing of matched normal (blood) tissue at 40X, and RNA-seq of tumour tissue at 40X coverage (>15 million reads). Whole-exome libraries were prepared using the Nextera Rapid Capture Exome Kit; exome capture was performed in pools of 3 and subjected to paired end 75 sequencing on a NovaSeq 6000 platform. RNA libraries were prepared using the TruSeq Stranded Total RNA HT kit with Ribo-Zero Gold as per manufacturer’s instructions and also subjected to paired end 75 sequencing on a NovaSeq 6000 platform. Uploaded bam files have been mapped to the hs37d5 human genome and processed using the standard GATK pipelines. Paired clinical, demographic, genotyping, and overall survival data for these patients are available from the associated publications or by request.

  Dataset EGAD00001009311

• Single Cell Genome Sequence for DLP+ library A95618B

  Single Cell Genome Sequence for Triple negative breast cancer patient-derived xenograft SA609 passage 3 on DLP+ library A95618B

  Dataset EGAD00001009316

• Single Cell Genome Sequence for DLP+ library A95628A

  Single Cell Genome Sequence for triple negative breast cancer patient SA1162SB on DLP+ library A95628A

  Dataset EGAD00001009319

• Single Cell Genome Sequence for DLP+ library A95632C

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA609 passage 6 on DLP+ library A95632C

  Dataset EGAD00001009322

• Normal sample for patient SA239

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA239

  Dataset EGAD00001009364

• NLG-LBC-05 ctDNA project sequencing data

  Low-pass whole-genome sequencing of pretherapy lymphoma cfDNA and targeted sequencing of cfDNA, tumor tissue and whole-blood samples of NLG-LBC-05 patient samples and cfDNA of nine subjects with no known cancer. Hybrid capture target enrichment; panel target and sequencing information provided in PMID:34932792. FASTQ files provided for targeted sequencing, separate sequencing runs per sample noted with prefix "run" if applicable. Sequences from DTX1 and KLHL6 targets are advised to be excluded from analyses due to PCR/plasmid contaminants of in-house origin.

  Dataset EGAD00001009337

• Normal sample for patient SA495

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA495

  Dataset EGAD00001009372

• Normal sample for patient SA425

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA425

  Dataset EGAD00001009371

• Normal sample for patient SA423

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA423

  Dataset EGAD00001009370

• Normal sample for patient SA291

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA291

  Dataset EGAD00001009368

• Normal sample for patient SA289

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA289

  Dataset EGAD00001009367

• Normal sample for patient SA286

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA286

  Dataset EGAD00001009366

• Normal sample for patient SA238

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA238

  Dataset EGAD00001009363

• Normal sample for patient SA221

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA221

  Dataset EGAD00001009362

• Tumour sample for patient SA280

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA280

  Dataset EGAD00001009361

• Tumour sample for patient SA291

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA291

  Dataset EGAD00001009360

• Tumour sample for patient SA289

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA289

  Dataset EGAD00001009359

• Tumour sample for patient SA286

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA286

  Dataset EGAD00001009358

• Tumour sample for patient SA495

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA495

  Dataset EGAD00001009357

• Tumour sample for patient SA425

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA425

  Dataset EGAD00001009356

• Tumour sample for patient SA423

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA423

  Dataset EGAD00001009355

• Tumour sample for patient SA300

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA300

  Dataset EGAD00001009354

• Tumour sample for patient SA239

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA239

  Dataset EGAD00001009353

• Tumour sample for patient SA238

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA238

  Dataset EGAD00001009352

• Tumour sample for patient SA221

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA221

  Dataset EGAD00001009351

• Tumour sample for patient SA683

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA683

  Dataset EGAD00001009350

• Tumour sample for patient SA682

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA682

  Dataset EGAD00001009349

• Tumour sample for patient SA681

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA681

  Dataset EGAD00001009348

• Tumour sample for patient SA680

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA680

  Dataset EGAD00001009347

• Tumour sample for patient SA679

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA679

  Dataset EGAD00001009346

• Tumour sample for patient SA678

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA678

  Dataset EGAD00001009345

• Tumour sample for patient SA677

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA677

  Dataset EGAD00001009344

• Tumour sample for patient SA676

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA676

  Dataset EGAD00001009343

• Tumour sample for patient SA675

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA675

  Dataset EGAD00001009342

• Tumour sample for patient SA674

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA674

  Dataset EGAD00001009341

• Tumour sample for patient SA673

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA673

  Dataset EGAD00001009340

• Normal sample for patient SA679

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA679

  Dataset EGAD00001009379

• Normal sample for patient SA680

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA680

  Dataset EGAD00001009380

• Normal sample for patient SA681

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA681

  Dataset EGAD00001009381

• Normal sample for patient SA682

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA682

  Dataset EGAD00001009382

• Normal sample for patient SA683

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA683

  Dataset EGAD00001009383

• Normal sample for patient SA678

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA678

  Dataset EGAD00001009378

• Normal sample for patient SA677

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA677

  Dataset EGAD00001009377

• Normal sample for patient SA673

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA673

  Dataset EGAD00001009373

• Normal sample for patient SA676

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA676

  Dataset EGAD00001009376

• Normal sample for patient SA674

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA674

  Dataset EGAD00001009374

• Normal sample for patient SA675

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA675

  Dataset EGAD00001009375

• Targeted DNA sequencing dataset for the study "Molecular profiling of EBV associated diffuse large B-cell lymphoma"

  We performed deep targeted DNA sequencing with a panel of 74 selected cancer-related genes previously identified to be recurrently mutated in EBV associated DLBCL. Sequencing was performed on a HiSeq platform (Illumina) with 250 bp paired-end reads. There are 68 FFPE samples in this targetedDNAseq-dataset with 46 unpaired tumors and 22 normals used as a panel of normals.

  Dataset EGAD00001009396

• Single Cell Genome Sequence for DLP+ library A118368B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA605 passage 3 on DLP+ library A118368B

  Dataset EGAD00001009432

• Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors.

  Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 58 HGSC tumours (53 primary tumours and 5 recurrent tumours) and 53 matched normals from 53 long-term survivor patients. Sequence libraries were generated from tumour and matched normal genomic DNA using the KAPA HyperPrep PCR-free library preparation kit (Roche) according to manufacturer’s instructions. Sequencing was carried out by the Kinghorn Centre for Clinical Genomics Sequencing Laboratory (Sydney, Australia) on the HiSeq X Ten System (Illumina) to a minimum base coverage of 30-fold for normal DNA and 60-fold for tumour DNA samples.

  Dataset EGAD00001009398

• Strand-seq of hematopoietic stem and progenitor cells along human aging

  Somatic mosaicism (SM), referring to the presence of somatic mutations in sub-populations of cells within healthy individuals, is associated with an increased risk of a variety of diseases, including cancer. Blood is at particularly high risk of SM, given its rapid turnover and functionally- heterogeneous cell-type composition. While the roles of point mutations and large-scale rearrangements in blood SM have been scrutinised in recent years, the functional impact of mosaic structural variants (mSVs) remains poorly understood. Using haplotype-resolved single-cell multi-omics based on Strand-seq technology, we explored the mSV landscape of human hematopoietic stem and progenitor cells (HSPCs).

  Dataset EGAD00001009402

• Genome-wide 5-mC profiling in plasma of metastatic ALK-rearranged non-small cell lung cancer (NSCLC) patients receiving tyrosine kinase inhibitor therapy.

  Longitudinal plasma samples (n = 79) of 21 ALK-positive NSCLC patients and 13 healthy donors were collected alongside 15 ALK-positive tumor tissue and 10 healthy lung tissue specimens. All plasma and tissue samples were analyzed by cell-free DNA methylation immunoprecipitation sequencing to generate genome-wide 5-mC profiles. Paired cfMeDIP on NextSeq 550 using KAPA Hyper Prep Kit was done.

  Dataset EGAD00001009412

• Cancers of Unknow Primary

  genetic data of 14 rigorously selected CUP samples

  Dataset EGAD00001009426

• Single Cell Genome Sequence for DLP+ library A110660A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA610 passage 3 on DLP+ library A110660A

  Dataset EGAD00001009430

• Tumour sample for patient SA296

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA296

  Dataset EGAD00001009520

• Single Cell Genome Sequence for DLP+ library A118790A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA610 passage 3, patient-derived xenograft SA1096C passage 1 on DLP+ library A118790A

  Dataset EGAD00001009437

• Single Cell Genome Sequence for DLP+ library A95670A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 5 on DLP+ library A95670A

  Dataset EGAD00001009448

• Single Cell Genome Sequence for DLP+ library A95670B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 6 on DLP+ library A95670B

  Dataset EGAD00001009449

• Single Cell Genome Sequence for DLP+ library A95722A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A95722A

  Dataset EGAD00001009451

• Single Cell Genome Sequence for DLP+ library A96109A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2 on DLP+ library A96109A

  Dataset EGAD00001009452

• Single Cell Genome Sequence for DLP+ library A96130A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA609 passage 8 on DLP+ library A96130A

  Dataset EGAD00001009454

• Single Cell Genome Sequence for DLP+ library A96154A

  Single Cell Genome Sequence for triple negative breast cancer patient SA1135, patient SA1162SA on DLP+ library A96154A

  Dataset EGAD00001009456

• Single Cell Genome Sequence for DLP+ library A96161A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 8 on DLP+ library A96161A

  Dataset EGAD00001009457

• Single Cell Genome Sequence for DLP+ library A96171A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2, patient-derived xenograft SA611 passage 3 on DLP+ library A96171A

  Dataset EGAD00001009458

• Single Cell Genome Sequence for DLP+ library A96173A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 15 on DLP+ library A96173A

  Dataset EGAD00001009459

• Single Cell Genome Sequence for DLP+ library A96174A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 11 on DLP+ library A96174A

  Dataset EGAD00001009460

• Single Cell Genome Sequence for DLP+ library A96175A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 7 on DLP+ library A96175A

  Dataset EGAD00001009461

• Single Cell Genome Sequence for DLP+ library A96177C

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A96177C

  Dataset EGAD00001009462

• Single Cell Genome Sequence for DLP+ library A96180A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A96180A

  Dataset EGAD00001009463

• Single Cell Genome Sequence for DLP+ library A96187A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 11, patient-derived xenograft SA609 passage 7 on DLP+ library A96187A

  Dataset EGAD00001009464

• Normal sample for patient SA597

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA597

  Dataset EGAD00001009572

• Single Cell Genome Sequence for DLP+ library A96212B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 7 on DLP+ library A96212B

  Dataset EGAD00001009476

• Single Cell Genome Sequence for DLP+ library A96213A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 6, cell line SA1090 on DLP+ library A96213A

  Dataset EGAD00001009477

• Single Cell Genome Sequence for DLP+ library A98181A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA919 passage 7, patient-derived xenograft SA1050A passage 1 on DLP+ library A98181A

  Dataset EGAD00001009479

• Single Cell Genome Sequence for DLP+ library A98240A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA530 passage 3 on DLP+ library A98240A

  Dataset EGAD00001009480

• CASCADE germline whole genome sequencing data

  High-coverage whole genome sequencing data (median coverage: 23.5X, range: 14.14X-32.62X)) from white blood cells of the patients from isolated buffy coat of the blood drawn postmortem from patients who participated in the CASCADE rapid autopsy program and died of metastatic castration resistant prostate cancer.

  Dataset EGAD00001009493

• Tracking the evolution of esophageal squamous cell carcinoma under dynamic immune selection by multi-omics sequencing

  Intratumoral heterogeneity (ITH) has been linked to decreased efficacy of clinical treatments. However, although genomic ITH has been characterized in genetic, transcriptomic and epigenetic alterations are hallmarks of esophageal squamous cell carcinoma (ESCC), the extent to which these are heterogeneous in ESCC has not been explored in a unified framework. Further, the extent to which tumor-infiltrated T lymphocytes (TILs) are directed against cancer cells, but how the immune infiltration acts as a selective force to shape the clonal evolution of ESCC is unclear. In this study, we perform multi-omic sequencing on 186 samples from 36 primary ESCC patients. Through multi-omics analyses, it is discovered that genomic, epigenomic, and transcriptomic ITH are underpinned by ongoing chromosomal instability. Based on the RNA-seq data, we observe diverse levels of immune infiltrate across different tumor sites from the same tumor. We reveal genetic mechanisms of neoantigen evasion under distinct selection pressure from the diverse immune microenvironment. Overall, our work offers an avenue of dissecting the complex contribution of the multi-omics level to the ITH in ESCC and thereby enhances the development of clinical therapy.

  Dataset EGAD00001009482

• Single Cell Genome Sequence for DLP+ library A95621B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2 on DLP+ library A95621B

  Dataset EGAD00001009487

• Normal sample for patient SA296

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA296

  Dataset EGAD00001009566

• CASCADE low-pass whole genome sequencing data

  Low-pass whole genome sequencing data (median coverage: 0.37X, range: 0.07X-5.8X) from diagnostic formalin-fixed paraffin-embedded tissue and fresh frozen postmortem tissues from ten organs and postmortem blood from patients who participated in the CASCADE rapid autopsy program and died of metastatic castration resistant prostate cancer. For samples CA27_11, CA34_10, CA35_5, CA35_6, CA36_3, CA36_11, CA63_13, CA63_34, CA76_4, CA76_11, CA79_4, CA83_14 and CA83_26, we subsampled (using `samtools view -s 0.01` after mapping) from respective high coverage data from "CASCADE tumour high-coverage whole genome sequencing data" dataset.

  Dataset EGAD00001009494

• CASCADE tumour high-coverage whole genome sequencing data

  High coverage whole genome sequencing data (total: 22; median coverage: ~56X; range: 27X – 82X) from fresh frozen postmortem tissues harvested from patients who participated in the CASCADE rapid autopsy program and died of metastatic castration resistant prostate cancer.

  Dataset EGAD00001009491

• CASCADE tumour transcriptome data

  RNA-Seq data from 20 fresh-frozen postmortem samples from three patients who participated in the CASCADE rapid autopsy program and died of metastatic castration resistant prostate cancer.

  Dataset EGAD00001009492

• Transcriptomic Profile of Colorectal Cancer Samples

  Samples encompass primary colorectal tumors or metastasis of 75 patients, collected by Medical Pathologists from surgically removed specimens. Tissues were embedded in optimal cutting temperature (OCT) medium, snapshot frozen in liquid nitrogen within 40 minutes of collection and preserved at -80ºC. Samples were collected between June 2010 and October 2017 as part of a prospective biobanking project.

  Dataset EGAD00001009506

• Differential Presence of Exons in Cell-Free DNA

  Differential Presence of Exons in Cell-Free DNA Reveals Different Patterns in Colorectal Cancer Between Metastatic, Non-Metastatic Patients and Healthy Donors. 159 samples, Illumina sequencing technology.

  Dataset EGAD00001009510

• Normal sample for patient SA284

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA284

  Dataset EGAD00001009562

• Normal sample for patient SA285

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA285

  Dataset EGAD00001009563

• Normal sample for patient SA290

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA290

  Dataset EGAD00001009564

• Normal sample for patient SA294

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA294

  Dataset EGAD00001009565

• Normal sample for patient SA399

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA399

  Dataset EGAD00001009567

• Normal sample for patient SA400

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA400

  Dataset EGAD00001009568

• Normal sample for patient SA409

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA409

  Dataset EGAD00001009569

• Normal sample for patient SA420

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA420

  Dataset EGAD00001009570