7790 results for "canc*"

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• Genome and transcriptome sequence data from a cholangiocarcinoma patient

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

  Dataset EGAD00001005838

• Genome and transcriptome sequence data from a metastatic choroid melanoma patient

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

  Dataset EGAD00001005839

• Genome and transcriptome sequence data from a carcinoma primary unknown origin patient

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

  Dataset EGAD00001005840

• Genome and transcriptome sequence data from a pancreatic cancer patient

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

  Dataset EGAD00001005841

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

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

  Dataset EGAD00001005842

• Genome and transcriptome sequence data from a metastatic gastrointestinal stromal tumor patient

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

  Dataset EGAD00001005843

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

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

  Dataset EGAD00001005844

• Genome and transcriptome sequence data from a metastatic unknown primary cancer patient

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

  Dataset EGAD00001005845

• Genome and transcriptome sequence data from a cavernous sinus meningioma patient

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

  Dataset EGAD00001005846

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

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

  Dataset EGAD00001005847

• Genome and transcriptome sequence data from a chondrosarcoma patient

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

  Dataset EGAD00001005848

• Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of the lung patient

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

  Dataset EGAD00001005850

• Genome and transcriptome sequence data from a metastatic melanoma patient

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

  Dataset EGAD00001005851

• WES of blood and patient-matched cecum tumor tissue samples, Panel NGS of patient-matched cfDNA

  WES using IDT xGen Research Exome on Illumina NovaSeq 2x150bp: Normal sample (buffy coat), cecum tumor biopsy at diagnosis, ileocecal valve region tumor sample at week 19, pericolonic metastasis at week 19, lymph node metastasis at week 19, peritoneal metastasis at week 19. Week 19 samples from hemicolectomy. Deep coverage cfDNA NGS using PanCeq pan-cancer panel on NextSeq 2x150bp: week 2 and week 10.

  Dataset EGAD00001005737

• UCSF Pediatric Bithalamic Glioma Genome Project

  Targeted next-generation sequencing of 13 pediatric bithalamic diffuse gliomas. BAM files of targeted next-generation DNA sequencing data of 13 pediatric gliomas, with multi-region sequencing data from 2 of these cases (17 total tumor samples). Genomic DNA was extracted from formalin-fixed, paraffin-embedded blocks of tumor tissue from using the QIAamp DNA FFPE Tissue Kit (Qiagen). Capture-based next-generation DNA sequencing was performed at the University of California, San Francisco Clinical Cancer Genomics Laboratory, using an assay that targets all coding exons of 480 cancer-related genes, select introns of 47 genes, and TERT promoter with a total sequencing footprint of 2.8 Mb (UCSF500 Cancer Panel). Sequencing libraries were prepared from genomic DNA, and target enrichment was performed by hybrid capture using a custom oligonucleotide library (Nimblegen SeqCap EZ Choice). Captured libraries were sequenced as paired-end 100 bp reads on an Illumina HiSeq 2500 instrument. Duplicate sequencing reads were removed computationally to allow for accurate allele frequency determination and copy number calling.

  Dataset EGAD00001005499

• Sequencing files for "A Unifying Paradigm for Transcriptional Heterogeneity and Squamous Features in Pancreatic Ductal Adenocarcinoma."

  Files from DNA and RNA sequencing from primary tumors and metastases from pancreatic cancer patients along with matched normal tissues.

  Dataset EGAD00001005519

• Bam files for a metastatic bladder cancer patient with BAP1 variants

  This dataset consists of three bam files (two cell-free DNA and one germline DNA) from a metastatic bladder cancer patient with BAP1 variants. Bam files were generated from targeted Illumina sequencing data.

  Dataset EGAD00001005520

• Heterogeneous metabolic signatures are linked to cancer cell differentiation in colorectal cancer

  Colorectal cancer (CRC) is characterized by functional intratumor heterogeneity that shares many similarites with the hierarchical organization of the normal intestinal epithelium. In order to relate transcriptional subtypes to functional tumor cell heterogeneity we applied scRNA-seq to 12 patient-derived CRC spheroid cultures. We identified shared expression programs that relate to intestinal lineages and revealed metabolic signatures that are linked to cancer cell differentiation. In addition, we validated and complemented sequencing results by quantitative microscopy using live-dyes and multiplexed RNA fluorescence in situ hybridization, thereby revealing metabolic compartmentalization and potential cell-cell interactions. Finally, we demonstrate functional differences between metabolically distinct lineage subtypes that might have strong implications for future treatment strategies of CRC.

  Dataset EGAD00001005524

• Ovarian cancer organoid biobank - followup

  WGS of more samples in the ovarian cancer organoid biobank dataset.

  Dataset EGAD00001005707

• MDA whole exome sequencing data for bladder cancer

  46 BAM files from 23 urothelial bladder cancer patients on an immunotherapy clinical trial. PBMC normal samples and solid tumor samples are paired. Alignment was done by BWA with reference genome hg19.

  Dataset EGAD00001005712

• Neoantigen study samples for prostate cancer

  Whole exome sequencing and RNA sequencing data from 30 patients with prostate cancer.

  Dataset EGAD00001005713

• DESIGN-NKI RNA-seq

  RNA-Seq data of 36 HPV-negative HNSCC specimens from patients treated at The Netherlands Cancer Institute, Amsterdam. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for polyA mRNA sequencing.

  Dataset EGAD00001005715

• DESIGN-NKI low coverage WGS

  Low-coverage whole genome sequencing data of 37 HPV-negative HNSCC specimens from patients treated at The Netherlands Cancer Institute. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for WGS to a depth of approx. 0.5X.

  Dataset EGAD00001005718

• NKI-MET HNSCC RNA-Seq

  RNA-Seq data of 8 HNSCC specimens from patients diagnosed with metastatic disease at The Netherlands Cancer Institute, Amsterdam. Primary HNSCC biopsy samples obtained prior to treatment were used for polyA mRNA sequencing.

  Dataset EGAD00001005720

• ART-NKI II HNSCC RNA-Seq

  RNA-Seq data of 25 HNSCC specimens from patients treated at The Netherlands Cancer Institute, Amsterdam and enrolled in the ARTFORCE trial. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for polyA mRNA sequencing.

  Dataset EGAD00001005721

• DESIGN II HNSCC RNA-Seq

  RNA-Seq data of 28 HPV-negative HNSCC specimens from patients treated at the Netherlands Cancer Institute (Amsterdam), VU Medical Centre (Amsterdam) or MAASTRO Clinic (Maastricht) in The Netherlands. HNSCC biopsy samples were obtained prior to treatment (chemo-radiotherapy) for the prospective study within the DESIGN project and used for polyA mRNA sequencing.

  Dataset EGAD00001005722

• Illumina short-reads and 10X Genomics linked-reads sequencing data for MCF7 and a primary breast tumor sample

  This dataset contains whole genome sequencing data from Illumina short-reads sequencing (2X150bp) and 10X Genomics linked-reads sequencing. Both the sequencing technologies were used to sequence MCF7 cell line and a primary breast triple-negative cancer sample. The fastq of paired-end reads for both the samples sequenced with both the technologies is available.

  Dataset EGAD00001005724

• Whole exome sequencing of advanced gastric cancer

  To define the cellular characteristics of malignant ascites of advanced gastric cancer patients and search for therapeutic strategies, we obtained 5 malignant ascites and 1 cerebrospinal fluid from five patients with gastric cancer. We analyzed single-cell RNA-seq data of 180 cells from 4 malignant ascites and 1 cerebrospinal fluid metastasis using Fluidigm® C1™ System. Whole exome sequencing data was also generated from blood or tumor tissue.

  Dataset EGAD00001005740

• GATA6 expression distinguishes classical and basal-like subtypes in advanced pancreatic cancer.

  PURPOSE: To determine the impact of basal-like and classical subtypes in advanced PDAC and to explore GATA6 expression as a surrogate biomarker. EXPERIMENTAL DESIGN: Within the COMPASS trial patients proceeding to chemotherapy for advanced PDAC undergo tumour biopsy for RNA sequencing. Overall response rate (ORR) and overall survival (OS) were stratified by subtypes and according to chemotherapy received. Correlation of GATA6 with the subtypes using gene expression profiling, in situ hybridization (ISH) were explored. RESULTS: Between December 2015-May 2019, 195 patients (95%) had enough tissue for RNA sequencing; 39 (20%) were classified as basal-like and 156 (80%) as classical. RECIST response data were available for 157 patients; 29 basal-like and 128 classical where the ORR was 10% vs. 33% respectively (p=0.02). In patients with basal-like tumours treated with modified FOLFIRINOX (mFFX) (n=22) the progression rate was 60% compared to 15% in classical PDAC (p= 0.0002). Median OS in the intention to treat population (n=195) was 9.3 months for classical vs. 5.9 months for basal-like PDAC (HR 0.47 95% CI 0.32-0.69, p=0.0001). GATA6 expression by RNAseq highly correlated with the classifier (p<0.001) and ISH predicted the subtypes with sensitivity of 89% and specificity of 83%. In a multivariable analysis, GATA6 expression was prognostic (p=0.02). In exploratory analyses, basal-like tumours, could be identified by keratin 5, were more hypoxic and enriched for a T cell inflamed gene expression signature. CONCLUSIONS: The basal-like subtype is chemoresistant and can be distinguished from classical PDAC by GATA6 expression.

  Dataset EGAD00001006081

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Oesophageal adenocarcinoma

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. This dataset contains all the data available for this study on 2021-09-27.

  Dataset EGAD00001006083

• Reliable detection of somatic mutations in solid tissues by laser-capture microdissection and low-input DNA sequencing

  Somatic mutations accumulate in healthy tissues as we age, giving rise to cancer and potentially contributing to ageing. To study somatic mutations in non-neoplastic tissues, we developed a series of protocols to sequence the genomes of small populations of cells isolated from histological sections. Here, we describe a complete workflow that combines laser-capture microdissection (LCM) with low-input genome sequencing, whilst circumventing the use of whole-genome amplification (WGA). The protocol is subdivided broadly into 4 steps: tissue processing, LCM, low-input library generation and mutation calling and filtering. The tissue processing and LCM steps are provided as general guidelines which may require tailoring based on the specific requirements of the study at hand. Our protocol for low-input library generation utilises enzymatic rather than acoustic fragmentation to generate WGA-free whole-genome libraries. Finally, the mutation calling and filtering strategy has been adapted from previously published protocols to account for artefacts introduced via library creation. To date, we have used this workflow to perform targeted and whole-genome sequencing of small populations of cells (typically 100-1,000 cells) in thousands of microbiopsies from a wide range of human tissues. The low-input DNA protocol is designed to be compatible with liquid handling platforms and make use of equipment and expertise standard to any core sequencing facility. However, obtaining low-input DNA material via LCM requires specialized equipment and expertise. The entire protocol from tissue reception through whole-genome library generation can be accomplished in as little as a week, though 2-3 weeks would be a more typical turnaround time.

  Dataset EGAD00001006088

• AT-AML samples dataset

  Serial samples from one AT-AML patient as described in publication Goldgraben et al Pediatric Blood & Cancer 2020. Whole exome sequencing of a AT-'germline' blood sample, one bone marrow sample (at AML diagnosis) and 3 AML blood samples. Library preped using the Illumina Nextera Rapid Capture Exome Enrichment Kit, and sequenced as PE150 on HiSeq4000. Provided: 5 BAM files (GRCh37); 2 VCF analyses (germline and somatic)

  Dataset EGAD00001006090

• Whole exome sequencing data from non-small-cell lung cancer patients receiving immunotherapy prognosis

  The plasma samples and white blood cell samples were collected from 30 non-small-cell lung cancer patients and 3 healthy individuals. The solid tumor biopsy samples from 14 patients (a subset of the 30 patients) were collected. The cfDNA was extracted from their plasma samples using the QIAamp circulating nucleic acid kit from QIAGEN (Germantown, MD). The cfDNA WES library was constructed with the SureSelect XT HS kit from Agilent Technologies (Santa Clara, CA) according to the manufacturer’s protocol. In brief, 10ng of cfDNA was used as input material. After end repair/dA-tailing of cfDNA, the adaptor was ligated. The ligation product was purified with Ampure XP beads (Beckman-Coulter, Atlanta, GA) and the adaptor-ligated library was amplified with index primer in 10-cycle PCR. The amplified library was purified again with Ampure XP beads, and the amount of amplified DNA was measured using the Qubit 1xdsDNA HS assay kit (ThermoFisher, Waltham, MA). 700-1000 ng of DNA sample was hybridized to the Agilent SureSelect Human All Exon V6 (Agilent) capture library and pulled down by streptavidin-coated beads. After washing the beads, the DNA library captured on the beads was re-amplified with 10-cycle PCR. The final libraries were purified by Ampure XP beads. The library concentration was measured by Qubit, and the quality was further examined with Agilent Bioanalyzer before the final step of 2x150bp paired-end sequencing on the Illumina HiSeq X10 platform (Illumina) at an average coverage of 200. Whole-exome capture libraries of genomic DNA of the 30 non-small cell lung cancer patients were constructed via Roche SeqCap EZ Exome V3 (Roche); whole-exome capture libraries of genomic DNA of the 3 healthy individuals were constructed via Agilent SureSelect Human All Exon V6 (Agilent). Enriched exome libraries were sequenced on the Illumina HiSeq 3000 platform (Illumina) to generate 2x100bp paired-end reads at an average coverage of 200.

  Dataset EGAD00001006096

• sWGS of CRC patients

  Paired end (47/98) and single end (51/98) shallow whole genome sequencing (sWGS) data for the identification of somatic copy number alterations (SCNA) and the estimation of tumor fractions in plasma DNA of colorectal cancer (CRC) patients.

  Dataset EGAD00001006101

• Partner independent fusion gene detection by multiplexed CRISPR Cas9 enrichment and long read Nanopore sequencing (FUDGE)

  Targeted long-read nanopore sequencing. Abstract: Fusion genes are hallmarks of various cancer types and important determinants for diagnosis, prognosis and treatment. Fusion gene partner choice and breakpoint-position promiscuity restricts diagnostic detection, even for known and recurrent configurations. To accurately and impartially identify fusions, we developed FUDGE: FUsion Detection from Gene Enrichment. FUDGE couples target-selected and strand-specific CRISPR/Cas9 activity for fusion gene driver enrichment - without prior knowledge of fusion partner or breakpoint-location – to long-read Nanopore sequencing with the bioinformatics pipeline NanoFG. FUDGE has flexible target-loci choices and enables multiplexed enrichment for simultaneous analysis of several genes in multiple samples in one sequencing run. We observe on-average 665 fold breakpoint-site enrichment and identify nucleotide resolution fusion breakpoints - within two days. The assay identifies cancer cell line and tumor sample fusions irrespective of partner gene or breakpoint-position. FUDGE is a rapid and versatile fusion detection assay, providing unparalleled opportunity for diagnostic pan-cancer fusion detection.

  Dataset EGAD00001006111

• Somatic mutation and clonal evolution in the human bladder_WGS (2020-05-05)

  In this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The data in this study will be generated by whole-genome sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006113

• Somatic mutation and clonal evolution in the human bladder_TGS (2020-05-05)

  In this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The study includes targeted sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006114

• Somatic mutation and clonal evolution in the human bladder Novaseq (2020-05-05)

  In this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The data in this study will be generated by whole-genome sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006116

• Somatic mutation and clonal evolution in the human bladder_WES (2020-05-05)

  In this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The data in this study will be generated by whole-exome sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006115

• Somatic mutation and clonal evolution in the human bladder WES-NOVASEQ (2020-05-05)

  n this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The study includes targeted sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006117

• Rio Dataset

  Homologous recombination DNA repair deficiency and PARP inhibition activity in primary triple negative breast cancer. RNA-Seq data for paired baseline and end of treatment samples

  Dataset EGAD00001006120

• Phylogenetic reconstruction of breast cancer

  This dataset contains the fastq files used for this study

  Dataset EGAD00001006121

• Activating AKT1 and PIK3CA mutations in metastatic castration-resistant prostate cancer

  This dataset contains all available targeted exon sequencing bam files from our study, "Activating AKT1 and PIK3CA mutations in metastatic castration-resistant prostate cancer". Patient identifiers are denoted by the first segment of the sample aliases (e.g. "P1"), and additional information is appended to reflect which serial sample is referenced (1st, 2nd, 3rd, etc.), and whether the sample represents cell-free DNA ("cfdna") or paired white-blood cell control ("WBC"). All samples were sequenced using Illumina technology.

  Dataset EGAD00001006122

• Epigenomic profile of diverse cancer

  The samples are from patients of multiple cancer types. The library preparation protocol was developed by the laboratory. The DNA libraries were then sequenced with 150bp paired-end reads.

  Dataset EGAD00001006124

• Competitive selection of somatic mutant clones in normal human skin varies with body site

  The risk of getting non-melanoma skin cancer varies over 40-fold across the body. Here we map mutations in normal skin in high and low risk sites in normal donors and those with an increased risk of skin cancer. The density of mutations varied widely, with evidence of positive and negative genetic selection.  Regional differences in mutational signatures in high and low cancer risk sites and preferential selection of mutants of TP53 in high risk skin and FAT1 in lower risk skin were observed. 10% of clones had copy number changes in cancer associated genes and the largest had multiple driver mutations with loss of heterozygosity. In hair follicles, a proposed site of origin of skin cancers, mutations in the upper follicle resembled adjacent skin, but the lower follicle was sparsely mutated. We conclude cancer risk reflects the efficiency of transformation of oncogenic mutants rather than the density of mutant clones.

  Dataset EGAD00001006194

• Processed RNA-seq data for clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint. This dataset includes the TPM and raw counts tables.

  Dataset EGAD00001006199

• Phenotype data for clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint. This dataset includes clinical phenotype data.

  Dataset EGAD00001006200

• Genotype data for clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint. This dataset includes the processed data from FMOne.

  Dataset EGAD00001006201

• Raw RNA-seq data for clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint. This dataset includes the raw RNA-seq data.

  Dataset EGAD00001006205

• Elevated somatic mutation burdens in normal human cells due to defective DNA polymerases

  Mutation accumulation over time in normal somatic cells contributes to cancer development and is proposed as a cause of ageing. DNA polymerases POLE and POLD1 replicate DNA with high fidelity during normal cell divisions. However, in some cancers defective proofreading due to acquired mutations in the exonuclease domains of POLE or POLD1 causes markedly elevated somatic mutation burdens with distinctive mutational signatures. POLE and POLD1 exonuclease domain mutations also cause familial cancer predisposition when inherited through the germline. Here, we sequenced normal tissue DNA from individuals with germline POLE or POLD1 exonuclease domain mutations. Increased mutation burdens with characteristic mutational signatures were found to varying extents in all normal adult somatic cell types examined, during early embryogenesis and in sperm. Mutation burdens were further markedly elevated in neoplasms from these individuals. Thus human physiology is able to tolerate ubiquitously elevated mutation burdens. Indeed, with the exception of early onset cancer, individuals with germline POLE and POLD1 exonuclease domain mutations are not reported to show abnormal phenotypic features, including those of premature ageing. The results, therefore, do not support a simple model in which all features of ageing are attributable to widespread cell malfunction directly resulting from somatic mutation burdens accrued during life.

  Dataset EGAD00001006212