7342 results for "canc*"

in 116.89 milliseconds.

• Tumour sample for patient SA400

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

  Dataset EGAD00001009576

• Tumour sample for patient SA290

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

  Dataset EGAD00001009577

• Tumour sample for patient SA288

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA288

  Dataset EGAD00001009578

• Tumour sample for patient SA299

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA299

  Dataset EGAD00001009579

• Tumour sample for patient SA095

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA095

  Dataset EGAD00001009580

• Tumour sample for patient SA415

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA415

  Dataset EGAD00001009581

• Normal sample for patient SA718

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA718

  Dataset EGAD00001009582

• Normal sample for patient SA720

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA720

  Dataset EGAD00001009583

• Tumour sample for patient SA718

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA718

  Dataset EGAD00001009584

• Tumour sample for patient SA720

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA720

  Dataset EGAD00001009585

• Normal sample for patient SA1017

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1017

  Dataset EGAD00001009586

• Normal sample for patient SA1026

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009587

• Normal sample for patient SA1027

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009588

• Normal sample for patient SA1028

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009589

• Normal sample for patient SA1040

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009590

• Normal sample for patient SA1064

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009591

• Normal sample for patient SA1065

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009592

• Normal sample for patient SA1069

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009593

• Normal sample for patient SA1073

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009594

• Normal sample for patient SA1074

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009595

• Normal sample for patient SA576

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009596

• Normal sample for patient SA610

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009597

• Normal sample for patient SA992

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009598

• Normal sample for patient SA994

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

  Dataset EGAD00001009599

• Normal sample for patient SA288

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA288

  Dataset EGAD00001009601

• Normal sample for patient SA299

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA299

  Dataset EGAD00001009602

• Normal sample for patient SA415

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA415

  Dataset EGAD00001009603

• Normal sample for patient SA666

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA666

  Dataset EGAD00001009604

• Tumour sample for patient SA1065

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009605

• Tumour sample for patient SA1064

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009606

• Tumour sample for patient SA1026

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009607

• Tumour sample for patient SA1069

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009608

• Tumour sample for patient SA1017

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1017

  Dataset EGAD00001009609

• Tumour sample for patient SA1027

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009610

• Tumour sample for patient SA1073

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009611

• Tumour sample for patient SA1074

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009612

• Tumour sample for patient SA576

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009613

• Tumour sample for patient SA1028

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009614

• Tumour sample for patient SA1040

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009615

• Tumour sample for patient SA992

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009616

• Tumour sample for patient SA610

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009617

• Normal sample for patient SA416

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA416

  Dataset EGAD00001009618

• Normal sample for patient SA1070

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009619

• Tumour sample for patient SA1070

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009620

• Tumour sample for patient SA997

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA997

  Dataset EGAD00001009621

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010113

• MutWP5: CRUK Mutographs of Cancer: Cancer Mastectomy (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 40 women who underwent mastecomies due to breast cancer. LCM and sequencing will be conducted on both normal, unaffected breast, and, where possible, tumour tissue. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue, and compare findings between the normal and associated cancer tissues. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those who are BRCA carriers. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010114

• FFPE, buffycoat and cell free DNA from N. German esophagus cancer patients - 2021

  28 patients. Cell-free DNA and leukocyte DNA, both from before any neoadjuvant treatment. Tumor FFPE tissue (plus metastasic tissue for some patients) from after any neoadjuvant treatment. Some cfDNA from follow-up time points (e.g. after neodj. treatment or in the clinical course). IDT xgen Pan-Cancer panel for hybridisation capture. Illumina TruSeq library prep for cfDNA and leukocyte DNA, Illumina DNA Prep for FFPE DNA. n=150 libraries in total.

  Dataset EGAD00001010081

• MutWP5: CRUK Mutographs of Cancer: BRCA Carriers (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastecomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010112

• untargeted whole genome sequencing - Evaluation and correction of GC biases in cell-free DNA at the fragment level

  These are aligned paired-end reads from Illumina NovaSeq 6000 whole-genome sequencing of 4 cfDNA samples extracted from blood plasma (plasma-Seq). Three samples from patients with breast cancer, prostate cancer, or colorectal cancer and one sample from a healthy individual were aligned to GRCh38 (GCA_000001405.15_GRCh38_no_alt_plus_hs38d1_analysis_set). The observed GC bias is different in each of these cfDNA samples which leads to different average GC content per sample. This bias is corrected using the GCparagon commandline tool.

  Dataset EGAD00001010100

• RNAseq analysis on primary sites Colorectal Cancer xenografts (PRX) samples

  Bank of primary sites (PRs) colorectal cancer of Patient Derived Xenografts (PDXs)

  Dataset EGAD00001010101

• MutWP5: CRUK Mutographs of Cancer: Lung: PD43291_Novaseq (WG)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutaiton burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010111

• Sequencing data for the manuscript "Early on-treatment changes in circulating tumor DNA fraction and response to enzalutamide or abiraterone in metastatic castration-resistant prostate cancer"

  This submission contains gzipped fastq files from paired-end targeted sequencing.

  Dataset EGAD00001010105

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (WG)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to women who had breast cancer and those who are BRCA 1/2 carriers. This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010109

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (Exome)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010110

• MutWP5: CRUK Mutographs of Cancer: BRCA Carriers (Exome)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastecomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010115

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (Exome)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010116

• MutWP5: CRUK Mutographs of Cancer: Cancer Mastectomy (Exome)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 40 women who underwent mastecomies due to breast cancer. LCM and sequencing will be conducted on both normal, unaffected breast, and, where possible, tumour tissue. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue, and compare findings between the normal and associated cancer tissues. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those who are BRCA carriers. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010117

• RNA sequencing data of 257 samples from the CORALLEEN trial

  Early-stage Luminal B breast cancer is frequent and is a major cause of breast cancer death due to its poor prognosis. Our proposal aims to study the biology behind the sensitivity and resistance of Luminal B breast cancer to chemotherapy (CHT) or a non-CHT regimen composed of hormone therapy in combination with ribociclib, a CDK4/6 inhibitor. To accomplish this, we first completed the SOLTI-1402 CORALLEEN phase II trial, a study where 106 patients with early-stage Luminal B breast cancer were randomized to standard neoadjuvant CHT for 6 months, or neoadjuvant letrozole and ribociclib for 6 months. After treatment, patients underwent surgery. The primary results of the study, which showed that the response rate to letrozole+ribociclib was similar to CHT, was reported (Prat et al; Lancet Oncol). Tumor biopsies were available at baseline, week 3 and surgery. A total of 257 samples were analyzed using the Illumina TruSeq Stranded Total RNA w/Ribo Zero Gold with MiSeq in TGL (Sequencer NovaSeq S4/PE/100x)

  Dataset EGAD00001010121

• Somatic mutation and clonal evolution normal breast tissue WGS

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >70 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumour development. . This dataset contains all the data available for this study on 2023-03-09.

  Dataset EGAD00001010123

• Breast cancer topographs

  Whole genome sequencing to identify subclonal variants for subsequent mapping back to fixed tissue specimens. . This dataset contains all the data available for this study on 2023-03-09.

  Dataset EGAD00001010124

• Genomics of acral lentiginous melanoma

  Melanoma is the most aggressive type of skin cancer, causing about 75% of dermatological cancer deaths. Acral lentiginous melanoma (ALM) is the most common subtype of melanoma in admixed Latin American populations, but very few tumour genomes and exomes, all from European-descent individuals, have been analysed across several studies. Because of this, the genomic landscape of ALM is mostly unknown. Our aim in this project is to define this landscape and identify driver somatic alterations by whole-exome sequencing a collection of ALM germline/tumour paired FFPE samples from the National Cancer Institute of Mexico. . This dataset contains all the data available for this study on 2023-03-09.

  Dataset EGAD00001010126

• 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

• Mechanism of action and resistance to Trastuzumab Deruxtecan in patients with metastatic breast cancer: the DAISY trial

  198 exome sequencing samples

  Dataset EGAD00001009853

• Germline WES data of children with pathogenic mutations in cancer predisposing genes

  This data set includes bam files (aligned to hg38) from the germline of children who have pathogenic mutations in cancer predisposing genes

  Dataset EGAD00001009854

• RNAseq of breast cancer bone metastases PDX treated to IACS

  Fastq files from RNAseq of breast cancer bone metastases PDX of tumor HBC-124 treated by IACS-010759 (4 samples) or not (4 samples).

  Dataset EGAD00001009857

• Dynamics of sequence and structural cell-free DNA landscapes in small-cell lung cancer

  The dataset for the study “Dynamics of sequence and structural cell-free DNA landscapes in small-cell lung cancer” includes 171 bam files from targeted next-generation sequencing (TEC-Seq) from plasma cell-free DNA and matched white blood cell DNA from 33 individuals with small cell lung cancer, alongside 10 bam files from whole exome sequencing of tumor and matched normal DNA for 5 individuals with small cell lung cancer.

  Dataset EGAD00001009860

• Genomic profiling of subcutaneous patient derived xenograft models of solid childhood cancer

  The pediatric cancer cohort in this study included 70 PDX models from 65 different individuals. This cohort included a total of 16 different pediatric solid tumor subtypes, including fourteen Wilms tumors, thirteen hepatoblastomas, thirteen osteosarcomas, ten germ cell tumors, four neuroblastomas, three clear cell sarcomas, two adrenal cortical carcinomas, two leydig cell tumors, two medulloblastomas, one embryonal rhabdomyosarcoma (ERMS), one Ewing sarcoma, one pleomorphic sarcoma, one adenocarcinoma, one glioblastoma, one mesothelioma and one ovarian tumor. Notably, we have five samples with multiple PDX models from same patient, including two cases with duplicates (564 and 564-Dup, 1796 and 1796-Dup), one case with two different metastasis (560-SM, 560-LM), one case with two blocks from same tumor (1939 and 1939-Dup), and one case with different primary tumor from same patient (2264 and 1932). We have a total of 353 sequencing data, including 82 RNA sequencing data (RNA-seq), 138 whole-exome sequencing (WES) and 135 low-pass whole-genome sequencing (WGS). For RNA-seq data, we have 61 PDXs and 21 PTs; for WES, we have 67 PDXs, 30 PTs and 40 matched normal germlines; for WGS, we have 64 PDXs, 30 PTs and 40 matched normal germlines. Of which, 19 PT-PDX paired RNA-seq, 28 paired PT-PDX paired WES and WGS were included.

  Dataset EGAD00001009863

• Prostate cancer m6A epitranscriptome

  Using meRIP-sequencing, we profiled N-6 methyladenosine (m6A) in a cohort of 148 primary prostate cancer samples as part of the Canadian Prostate Cancer Genome Network project (CPC-GENE). Paired-end sequencing of 150 bp reads were mapped to GRCh38.p13 using annotations from gencode.v34.chr_patch_hapl_scaff.annotation.gtf. Peaks were called using MeTPeak, joint peaks were identified and IP reads were quantitated, normalized and adjusted using Input reads to obtain estimates of m6A abundance.

  Dataset EGAD00001009869

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 3)

  Whole genome sequencing data of 9 high-grade serous carcinoma (HGSC) patients (55 samples) sequenced with HiSeq X Ten.

  Dataset EGAD00001009888

• patient-derived head and neck cancer organoids

  patient-derived head and neck cancer organoids

  Dataset EGAD00001010134

• WGS of high grade serous ovarian tumours and matched blood normals

  FASTQ reads for 34 matched tumour-normal WGS pairs for high grade serous ovarian cancer patients. Scottish HGSOC samples were collected via local Bioresource facilities at Edinburgh, Glasgow, Dundee and Aberdeen and stored in liquid Nitrogen until required. HGSOC patients were determined from pathology records and were included in the study where there was matched tumour and whole blood samples. Tumour samples were divided into two for DNA and RNA extraction and slivers of tissue were taken, fixed in formalin and embedded in paraffin wax (FFPE). Samples were only included if they were confirmed as HGSOC and there was greater than 40% tumour cellularity throughout the tumour, determined using H&sE staining of the FFPE sections and pathology review. Somatic DNA was extracted from the tumour and germline DNA was extracted from whole blood. Somatic DNA was extracted using the Qiagen DNeasy Blood and tissue kit (cat no 69504). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including RNase digestion step). Germline DNA was extracted from 1-3ml whole blood using the Qiagen FlexiGene kit (cat no 51206) following the manufacturers recommended protocol. The resulting DNA underwent quality control as follows: firstly, A260 and A280nm were measured on a Denovix DS-11 Fx to qualitatively illustrate A260/280nm and A260/230nm ratios as surrogate measures of DNA purity. A260/280 had to be 1.8 or greater and A260/230 had to be 2.0 or greater. Then, DNA was quantified using LifeTechnologies Qubit dsDNA BR kit (cat no Q32850) and we required a minimum of 50ul at 25ng/ul for WGS. Thirdly, DNA was diluted to 25ng/ul and a representative sample was loaded onto a 0.8% TAE gel, ran at 100v for 60mins and then imaged using a BioRad ChemiDoc imaging system to visualise the DNA quality.

  Dataset EGAD00001010135

• Differential methylation positions

  This dataset includes an analyzed DMP file that provide the information about differential methylation positions based on Illumina Infinium MethylationEPIC BeadChip. All samples (5 lung cancer cases vs. 5 benign lung disease controls) were obtained from bronchial washings at the site of the lesion under bronchoscopy manipulation. Of the five lung cancer cases, 3 are adenocarcinoma and 2 are squamous carcinoma.

  Dataset EGAD00001010147

• sWGS data of Pap test smears and tumor tissues

  Shallow whole-genome sequencing data divided into three groups: - sWGS data from Pap smears of patients with confirmed high grade serous ovarian cancer - sWGS data from matched tumor tissue (at diagnosis) from the same patients - sWGS data from Pap tests smears of healthy women

  Dataset EGAD00001010141

• NCCS-NSCLC-ITH2-WES

  NCCS-NSCLC-ITH2 dataset of 185 sectors from 48 patients with early-stage non-small cell lung cancer diagnosed in National Cancer Centre Singapore; these are paired-end, whole-exome and bulk RNA sequencing data, sequenced by Illumina HiSeq 4000/2000.

  Dataset EGAD00001010158

• WES and RNA-seq data of 11 cancer patients with multiple cancer types

  This dataset includes WES and RNAseq from 11 patients with metastatic melanoma, lung, kidney, and stomach cancers, enrolled in phase I clinical trials of TIL ACT (NCT03475134 & NCT04643574). WES was performed on matched cancer and healthy samples, whereas RNAseq was performed on cancer tissues, using Illumina HiSeq 2500/4000 and Illumina NextSeq 550 systems. Paired-end reads are provided in fastq format.

  Dataset EGAD00001010169

• Ither2 WXS dataset - Implementation of pediatric precision oncology into clinical practice: The individualized Therapies for Children with cancer program “iTHER”

  iTHER is a prospective national precision oncology program aiming to define tumor molecular profiles in children and adolescents with primary very high-risk, relapsed, or refractory pediatric tumors in order to identify relevant aberrations to inform treatment.

  Dataset EGAD00001010178

• Ither2 RNA-Seq dataset - Implementation of pediatric precision oncology into clinical practice: The individualized Therapies for Children with cancer program “iTHER”

  iTHER is a prospective national precision oncology program aiming to define tumor molecular profiles in children and adolescents with primary very high-risk, relapsed, or refractory pediatric tumors in order to identify relevant aberrations to inform treatment.

  Dataset EGAD00001010179

• HGSOC-celllines-SmartSeq3

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

  Dataset EGAD00001009798

• Distinct embryonic phylogenies and driver events of infant Wilms tumor - DNA

  Cancers of adults typically arise through progressive rounds of clonal diversification and intratumoral selective sweeps which generate a long mutational trunk with shorter subclonal branches. Here, we investigated whether tumors of young children exhibit the same phylogenetic configuration. We studied three infants, including two newborns, with the childhood kidney cancer, Wilms tumour, through whole genome sequencing of bulk tissues, of single cell derived organoids, and of microdissections. All three cancers exhibited unusual driver events, with tumours of newborns harbouring FOXR2 rearrangements, delineating a distinct variant of Wilms tumour. Phylogenetic analyses suggest that tumors were seeded in an early, possibly confined window of development. Unusually, following seeding there was extensive polyclonal diversification with little evidence of clonal sweeps, leading to a distinct phylogenetic configuration more reminiscent of normal tissues rather than of adult cancers. These findings indicate that some childhood cancers may diversify via unorthodox phylogenetic pathways.

  Dataset EGAD00001009812

• Distinct embryonic phylogenies and driver events of infant Wilms tumor - RNA

  Cancers of adults typically arise through progressive rounds of clonal diversification and intratumoral selective sweeps which generate a long mutational trunk with shorter subclonal branches. Here, we investigated whether tumors of young children exhibit the same phylogenetic configuration. We studied three infants, including two newborns, with the childhood kidney cancer, Wilms tumour, through whole genome sequencing of bulk tissues, of single cell derived organoids, and of microdissections. All three cancers exhibited unusual driver events, with tumours of newborns harbouring FOXR2 rearrangements, delineating a distinct variant of Wilms tumour. Phylogenetic analyses suggest that tumors were seeded in an early, possibly confined window of development. Unusually, following seeding there was extensive polyclonal diversification with little evidence of clonal sweeps, leading to a distinct phylogenetic configuration more reminiscent of normal tissues rather than of adult cancers. These findings indicate that some childhood cancers may diversify via unorthodox phylogenetic pathways.

  Dataset EGAD00001009813

• Concatenated long-read single-cell RNA sequencing of momentum biopsies of ovarian cancer patients

  Concatenated long-read single-cell RNA sequencing samples prepared using 10X and the HIT-scIsoSeq protocol. The sequencing was performed on Sequel II Pacbio machine. 3 ovarian cancer patients, 5 omentum biopsies samples: 3 metastasis samples (one per patient), 2 healthy samples (one per patient except Patient2). 4 bam files per metastasis sample, 2 bam files per healthy sample.

  Dataset EGAD00001009814

• Illumina single-cell RNA sequencing of omentum biopsies of ovarian cancer patients

  Illumina Novaseq paired-end single-cell RNA sequencing samples prepared using 10X Genomics platform. 3 ovarian cancer patients, 5 omentum biopsies samples: 3 HGSOC metastasis samples (one per patient), 2 healthy samples (one per patient except Patient2). 4 paired fastq files per sample.

  Dataset EGAD00001009815

• Tumor/Normal WXS and RNASeq from patients dosed with neoTCR T-cell therapy

  Sixteen patients with refractory solid cancers received up to three distinct neoTCR-transgenic cell products, each expressing a patient-specific neoTCR, in a cell dose-escalation, first-in-human phase 1 clinical trial (NCT03970382). Included are the tumor and normal WXS and tumor RNAseq for dosed patients.

  Dataset EGAD00001009830

• GELATO clinical trial RNA-Seq data (primary tumors and local recurrences)

  RNA-Seq data of 10 lobular breast cancer primary tumors and 3 local recurrences obtained from 11 unique patients from GELATO clinical trial. The included raw transcriptome sequencing data in fastq format was generated using Illumina NovaSeq 6000 from archived FFPE material.

  Dataset EGAD00001009835

• GELATO clinical trial RNA-Seq data (metastatic lesions)

  RNA-Seq data of 46 matched lobular breast cancer metastatic samples obtained from 21 unique patients from GELATO clinical trial assayed at three timepoints: at baseline (directly after patient randomization), pre-atezolizumab (after induction treatment with carboplatin for two weeks) and on atezolizumab (after two cycles of atezolizumab combined with carboplatin). The included raw transcriptome sequencing data in fastq format was generated using Illumina NovaSeq 6000 from fresh frozen material.

  Dataset EGAD00001009834

• GELATO clinical trial whole exome sequencing data (primary tumors and local recurrences)

  Paired-end whole exome sequencing of 10 lobular breast cancer primary tumors, 3 local recurrences and matched normal samples obtained from 10 unique patients from the GELATO clinical trial. The included raw sequencing data in fastq format was generated using Illumina NovaSeq 6000 from archived FFPE material (tumor data) and fresh frozen material (matched normal data).

  Dataset EGAD00001009836

• GELATO clinical trial whole exome sequencing data (metastatic lesions)

  Paired-end whole exome sequencing of 19 lobular breast cancer metastatic tumors and matched normal samples obtained from 19 unique patients from the GELATO clinical trial. The included raw sequencing data in fastq format was generated using Illumina NovaSeq 6000 from fresh frozen material.

  Dataset EGAD00001009837

• 15 Healthy controls, 25 conlonrectal cancer patients without liver metastasis and 24 conlonrectal cancer patients with liver metastasis (target capture)

  15 Healthy controls, 25 conlonrectal cancer patients without liver metastasis and 24 conlonrectal cancer patients with liver metastasis (target capture)

  Dataset EGAD00001009838