7296 results for "canc*"

in 31.56 milliseconds.

• Normal sample for patient SA671

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA671

  Dataset EGAD00001009256

• Normal sample for patient SA672

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA672

  Dataset EGAD00001009257

• Tumour sample for patient SA1035

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1035

  Dataset EGAD00001009258

• Tumour sample for patient SA994

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA994

  Dataset EGAD00001009259

• Tumour sample for patient SA604

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA604

  Dataset EGAD00001009260

• Tumour sample for patient SA605

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009261

• Tumour sample for patient SA609

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009262

• 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

• 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

  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

• Tumour sample for patient SA218

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA218

  Dataset EGAD00001009177

• Tumour sample for patient SA219

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA219

  Dataset EGAD00001009178

• Tumour sample for patient SA272

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA272

  Dataset EGAD00001009179

• Tumour sample for patient SA274

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA274

  Dataset EGAD00001009180

• Tumour sample for patient SA275

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA275

  Dataset EGAD00001009181

• Tumour sample for patient SA276

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA276

  Dataset EGAD00001009182

• Tumour sample for patient SA279

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA279

  Dataset EGAD00001009183

• Tumour sample for patient SA283

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA283

  Dataset EGAD00001009184

• Tumour sample for patient SA287

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA287

  Dataset EGAD00001009185

• Tumour sample for patient SA394

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA394

  Dataset EGAD00001009186

• Tumour sample for patient SA395

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA395

  Dataset EGAD00001009187

• Tumour sample for patient SA398

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA398

  Dataset EGAD00001009188

• Tumour sample for patient SA402

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA402

  Dataset EGAD00001009189

• Tumour sample for patient SA404

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA404

  Dataset EGAD00001009190

• Tumour sample for patient SA530

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA530

  Dataset EGAD00001009191

• Tumour sample for patient SA585

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA585

  Dataset EGAD00001009192

• Tumour sample for patient SA586

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA586

  Dataset EGAD00001009193

• Tumour sample for patient SA588

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA588

  Dataset EGAD00001009194

• Tumour sample for patient SA591

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA591

  Dataset EGAD00001009197

• Tumour sample for patient SA592

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA592

  Dataset EGAD00001009198

• Tumour sample for patient SA593

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA593

  Dataset EGAD00001009199

• 10x Genomics Single Cell Gene Expression for SA1035X7XB03502

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 7

  Dataset EGAD00001009151

• 10x Genomics Single Cell Gene Expression for SA535X4XB02498

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009152

• 10x Genomics Single Cell Gene Expression for SA1035X5XB03021

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 5

  Dataset EGAD00001009153

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03216

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009154

• 10x Genomics Single Cell Gene Expression for SA535X5XB02891

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009155

• 10x Genomics Single Cell Gene Expression for SA535X5XB02895

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009156

• 10x Genomics Single Cell Gene Expression for SA535X9XB03617

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 9

  Dataset EGAD00001009157

• 10x Genomics Single Cell Gene Expression for SA604X9XB02425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 9

  Dataset EGAD00001009159

• 10x Genomics Single Cell Gene Expression for SA610X3XB03802

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA610 passage 3

  Dataset EGAD00001009160

• MESA colorectal cancer cfDNA EM-seq dataset

  This dataset contains the methylation sequencing data of 60 nonCancer and 70 colorectal cancer cfDNA samples. The methylation library is constructed by using NEBNext Enzymatic-seq Kit.

  Dataset EGAD00001009165

• Tumour sample for patient SA590

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA590

  Dataset EGAD00001009196

• Tumour sample for patient SA589

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA589

  Dataset EGAD00001009195

• Colorectal cancer samples WES

  The whole exome was sequenced in two cancer-affected members (II:1 and III:1) of the family. The family subject of this study showed an autosomal dominant mode of CRC inheritance, fulfilling the Amsterdam I clinical criteria with three CRCs in two consecutive generations. The exome capture was performed using SureSelectXT Human All Exon V3 (51Mb, Agilent Technologies), and the library was sequenced on an Illumina HiSeq 2000 platform with paired-end reads of 101bp and a 50x average coverage depth.

  Dataset EGAD00001009170

• RNAseq analysis on metastatic Colorectal Cancer xenografts samples

  Bank of metastatic colorectal cancer (mCRC) of Patient Derived Xenografts (PDXs)

  Dataset EGAD00001009171

• Bulk RNA-seq of stromal cells from multiple cancer types

  We sorted CD45-CD44+CD90+ stromal cells from multiple tumor types and performed bulk RNA-sequencing.

  Dataset EGAD00001009176

• Tumour sample for patient SA595

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA595

  Dataset EGAD00001009200

• 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 SA300

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

  Dataset EGAD00001009369

• 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 SA280

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

  Dataset EGAD00001009365

• Normal sample for patient SA239

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

  Dataset EGAD00001009364

• 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

• Paired ONT and downsampled Illumina cfDNA dataset

  The dataset contains 12 lung cancer plasma cfDNA samples, 8 bladder cancer and 2 healthy control urine cfDNA samples collected in EDTA collection tubes. Shallow WGS was performed using both Oxford Nanopore Technologies' MinION platform with an R9.4.1 flow cell and the SQK-PBK004 kit (22 files) and Illumina Novaseq platform with an S4 flow cell in PE150bp configuration (2x22 files).

  Dataset EGAD00001009392

• 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

• 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

• KiCS cancer panel academic only data

  This data set contains KiCS cancer panel data for academic and for-profit use.

  Dataset EGAD00001009733

• KiCS cancer panel data for academic and for-profit use

  This data set contains KiCS cancer panel data for academic and for-profit use.

  Dataset EGAD00001009734

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 0)

  Whole genome sequencing data of 5 High-grade serous carcinoma (HGSC) patients (6 samples) sequenced with BGI.

  Dataset EGAD00001009738

• Whole genome sequencing

  Whole genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from 15 patients with homologous recombination deficiencies. The dataset includes fastq files from 56 HGSC tumours (1 primary, 1 relapse, 54 end-stage) and 15 matched normals. Sequence libraries were generated from tumour and matched normal genomic DNA using the KAPA HyperPrep PCR-free library preparation kit (Roche), or the Illumina TruSeq DNA Nano kit 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) or by the Australian Genome Research Facility (Melbourne, Australia) on an Illumina NovaSeq to a minimum base coverage of 30-fold for normal DNA and 60-fold for tumour DNA samples.

  Dataset EGAD00001009746

• Targeted DNA sequencing

  Targeted DNA sequencing of high-grade serous ovarian cancer (HGSC) tumour and normal samples from 15 patients with homologous recombination deficiencies. The dataset includes fastq files from 243 HGSC tumours (15 primary, 3 relapse, 225 end-stage) and 15 normals from 15 HGSC patients. Following target hybrid capture of 63 genes involved in DNA repair and response to treatment with an Agilent SureSelect XT panel, sequencing libraries were generated using the SureSelect XT Low Input Target Enrichment System (Agilent) as per the manufacturer's protocol. Libraries were sequenced on an Illumina NextSeq 500 at the Peter MacCallum Cancer Centre (Melbourne, Australia).

  Dataset EGAD00001009747

• S:CORT Stratification in COloRecTal cancer

  Colorectal cancer samples will be submitted for Illumina sequencing using a custom capture of 116 genes implicated in colorectal tumourigenesis. Driver mutations will be detected and ultimately correlated with phenotypic data.

  Dataset EGAD00001009760

• Single Cell Genome Sequence for DLP+ library A96141A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 8 on DLP+ library A96141A

  Dataset EGAD00001009777

• Single cell RNA-sequencing of tissues from NSCLC patients treated with chemoimmunotherapy

  The goal of this study is to characterize immune cell populations by single cell RNA-sequencing (scRNA-seq) in tumor and uninvolved normal tissues from non-small cell lung cancer (NSCLC) patients with resectable non-small cell lung cancer and who received neoadjuvant chemoimmunotherapy. scRNA-seq was performed on seven pairs of tumor and normal tissues as well as one lymph node (LN) sample. Data set includes pair-end fastq files for single cell RNA sequencing of 7 neo-immuno patients. (Total 15 samples and 50 runs).

  Dataset EGAD00001009778

• cfDNA from fractionated and paired unfractionated plasma samples of lung cancer patients

  Plasma from lung cancer patients from EDTA tubes was fractionated using size exclusion chromatography. Fractions 1-5, 7-11, 12-15, 16-20 were pooled, cfDNA was extracted from the fractions and paired unfractionated samples and PE150bp sequencing was performed on an Illumina Novaseq S4 flowcell. Samples are provided as raw reads without any prior processing.

  Dataset EGAD00001009780

• BCSA_Exome+SmartSeq3

  Multiple regions were cut out of each tumor extracted form breast cancer patients and two experiments were run: - Whole exome sequencing on each of the regions plus an adjacent normal tissue sample - Smart-Seq3 Single cell RNA sequencing on EPCAM+ CD45- sorted cells from different tumor regions

  Dataset EGAD00001009781

• Tumor Educated Platelets (TEPs) for breast cancer detection

  The TEP dataset consists of 549 Fastq samples which are divided into two experiments: a training data cohort, used to train the classifier, and a validation data cohort, used to assess classifier performance

  Dataset EGAD00001009790

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 2)

  Whole genome sequencing data of 8 High-grade serous carcinoma (HGSC) patients (20 samples) sequenced with HiSeq X Ten.

  Dataset EGAD00001009791

• CCMA

  This dataset has the processed WGS data for the cancer models in CCMA.

  Dataset EGAD00001009633

• RNAseq of primary mesothelioma cell lines

  Mesothelioma is an aggressive cancer associated with previous exposure to asbestos and dismal prognosis. Since a pemetrexed/cisplatin combination was introduced for treatment of mesothelioma, no new first- or second-line therapies have been discovered. Thus, to better understand what drives mesothelioma carcinogenesis and to identify potential targets for therapy, in this project we aim at performing RNAseq analysis of a panel of mesothelioma cells lines.

  Dataset EGAD00001009642

• WGS of primary mesothelioma cell lines

  Mesothelioma is an aggressive cancer associated with previous exposure to asbestos and dismal prognosis. Since a pemetrexed/cisplatin combination was introduced for treatment of mesothelioma, no new first- or second-line therapies have been discovered. Thus, to better understand what drives mesothelioma carcinogenesis and to identify potential targets for therapy, in this project we aim at performing WGS analysis of a panel of mesothelioma cells lines.

  Dataset EGAD00001009641

• Single Cell Genome Sequence for DLP+ library A96228A

  Single Cell Genome Sequence for Immortalized lymphoblastoid cell line GM18507 cell line SA928,Triple negative breast cancer patient-derived xenograft SA609 passage 2 patient-derived xenograft SA609 passage 2 on DLP+ library A96228A

  Dataset EGAD00001009645

• Single Cell Genome Sequence for DLP+ library A98247A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA530 passage 3 on DLP+ library A98247A

  Dataset EGAD00001009646

• Sequencing data for the manuscript "Multi-focal sampling of de novo metastatic prostate cancer reveals complex polyclonality and enables accurate clinical genotyping"

  This submission includes targeted and whole exome paired-end fastq files.

  Dataset EGAD00001009651

• ATAC-seq of metastatic prostate tumors

  This dataset includes Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data, in FASTQ format, from 70 metastatic castration-resistant prostate cancer tumor samples from the SU2C/PCF West Coast Dream Team (WCDT) project. The sequencing data is paired-end, 150 bp sequencing data from an Illumina NovaSeq 6000 machine. ATAC-seq libraries were prepared following the protocol described in Buenrostro et al. Nature Methods. 2013 (PMID: 24097267) and Corces et al. Nature Methods. 2017 (PMID: 28846090).

  Dataset EGAD00001009654

• Nanopore low-pass WGS of human brain tumors

  Nanopore low-pass WGS of human brain tumors for evaluation of DNA methylation-based classification of cancer

  Dataset EGAD00001009663

• Somatic mutations in facial skin from countries of contrasting skin cancer risk

  The incidence of non-melanoma skin cancer is 17-fold lower in Singapore compared to the UK1, despite Singapore receiving 2-3 times more year-round ultraviolet radiation (UV)2,3. The ageing epidermis of the skin comprises competing somatic mutant clones4,5, from which such cancers develop. We question if differences in keratinocyte skin cancer incidence are reflected in the mutational landscape by comparing ageing facial epidermis from donors of Singapore and the UK. We find UK skin to be a highly competitive, densely mutated landscape with 4-fold greater mutation burden compared to Singaporean skin and differences in clonal selection by country. We disproportionately observe multiple features common to keratinocyte skin cancers6,7,8 in UK skin, such as UV mutagenesis, copy number aberration and hotspot mutations (in particular TP53 R248W). We conclude that keratinocyte skin cancer incidence is reflected in the somatic clones of non-cancerous epidermis. Finally, we re-analyse squamous cell carcinoma exomes from Korea9 to show, even in low incidence populations, carcinogenesis is driven by UV damage.

  Dataset EGAD00001009666

• The University of Hong Kong Colon Cancer Ganciclovir Study WGS Data

  Human clonal intestinal organoids were treated with 1µM MMF (Roche), 20 μM GCV (Hainan Poly Pharm Co Ltd), or in combinations (1 µM MMF + 20 μM GCV or 1 µM MMF + 40 μM GCV) continuously for 4-6 weeks. DNA was extracted after drug treatment. WGS was performed with 150 bp PE sequencing at 30X using an Illumina Novaseq sequencer.

  Dataset EGAD00001009667

• Tumour sample for patient SA1058

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1058

  Dataset EGAD00001009673

• Normal sample for patient SA1058

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1058

  Dataset EGAD00001009672

• Tumour sample for patient SA998

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA998

  Dataset EGAD00001009674

• Variants from germline WES data of pediatric cancer patients

  This dataset contains germline variants (in .vcf format) from six pediatric cancer patients (sample IDs D1 - D6). WES data of the children and their parents was mapped to hg38. A consensus of four variant callers was used to obtain germline variants of the children.

  Dataset EGAD00001009678

• HGSOC-celllines-SmartSeq3

  Smart-seq3 scRNA-seq of cells from primary (OV2295) and metastatic (OV2295R2) high-grade serous ovarian cancer cell-line

  Dataset EGAD00001009798