5707 results for "canc*"

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• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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 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 SA409

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

  Dataset EGAD00001009569

• 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

• 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