8213 results for "canc*"

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• Normal sample for patient SA1073

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

  Dataset EGAD00001009594

• CASCADE low-pass whole genome sequencing data

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

  Dataset EGAD00001009494

• CASCADE tumour transcriptome data

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

  Dataset EGAD00001009492

• CASCADE germline whole genome sequencing data

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

  Dataset EGAD00001009493

• Tumour sample for patient SA234

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA234

  Dataset EGAD00001009525

• Tumour sample for patient SA278

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA278

  Dataset EGAD00001009526

• Transcriptomic Profile of Colorectal Cancer Samples

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

  Dataset EGAD00001009506

• Tumour sample for patient SA211

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA211

  Dataset EGAD00001009523

• Differential Presence of Exons in Cell-Free DNA

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

  Dataset EGAD00001009510

• 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 EGAD00001009513

• Tumour sample for patient SA230

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA230

  Dataset EGAD00001009524

• Tumour sample for patient SA232

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA232

  Dataset EGAD00001009522

• Tumour sample for patient SA597

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

  Dataset EGAD00001009521

• Tumour sample for patient SA296

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

  Dataset EGAD00001009520

• Tumour sample for patient SA420

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

  Dataset EGAD00001009519

• Tumour sample for patient SA409

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

  Dataset EGAD00001009518

• Tumour sample for patient SA533

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA533

  Dataset EGAD00001009517

• Tumour sample for patient SA101

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA101

  Dataset EGAD00001009527

• Tumour sample for patient SA212

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA212

  Dataset EGAD00001009528

• Tumour sample for patient SA214

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA214

  Dataset EGAD00001009529

• Tumour sample for patient SA224

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA224

  Dataset EGAD00001009530

• Tumour sample for patient SA225

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA225

  Dataset EGAD00001009531

• Tumour sample for patient SA226

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA226

  Dataset EGAD00001009532

• Tumour sample for patient SA228

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA228

  Dataset EGAD00001009533

• Tumour sample for patient SA229

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA229

  Dataset EGAD00001009534

• Tumour sample for patient SA231

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA231

  Dataset EGAD00001009535

• Tumour sample for patient SA233

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA233

  Dataset EGAD00001009536

• Tumour sample for patient SA237

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA237

  Dataset EGAD00001009537

• Tumour sample for patient SA271

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA271

  Dataset EGAD00001009538

• Tumour sample for patient SA277

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA277

  Dataset EGAD00001009539

• Tumour sample for patient SA284

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

  Dataset EGAD00001009540

• Tumour sample for patient SA399

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

  Dataset EGAD00001009541

• Tumour sample for patient SA294

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

  Dataset EGAD00001009542

• Tumour sample for patient SA285

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

  Dataset EGAD00001009543

• Normal sample for patient SA101

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA101

  Dataset EGAD00001009544

• 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

• META-PRISM

  A pan-cancer cohort of 1031 patients resistant to systemic therapies or with no approved therapeutic options. It includes whole-exome sequencing of 571 tumor and matched-normal samples, and transcriptome sequencing of 947 tumor samples. Biopsies were taken at entry into precision medicine trials, often after diagnosed resistance. Comprehensive clinical information is available for all patients and include patient age at biospy, tumor primary site and histological subtype, biopsy site, treatments received prior to biopsy, blood assessment results at biopsy, metastatic sites at biopsy, and survival time from the biopsy date.

  Dataset EGAD00001009684

• 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

• CRUK Accelerator: Pancreatic neuroendocrine tumours (panNETs) whole exome and RNAseq raw sequencing data

  Whole exome and RNASeq raw sequencing data for an individual with multiple pancreatic neuroendocrine tumours (panNETs). Age at diagnosis was 51. Tumour tissue and PBMCs were used for sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001009685

• TRACERx Reduced-representation bisulfite sequencing (RRBS)

  RRBS data from TRACERx non-small cell lung cancer (NSCLC) tumours and matched normal adjacent tissue. TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC).

  Dataset EGAD00001009707

• Human primary and metastatic colorectal cancer (CRC) samples

  Bank of human both primary and metastastic colorectal cancer sample RNAseq

  Dataset EGAD00001009712

• pan-cancer plasma cfRNA

  Dataset contains mRNA capture sequencing data from plasma of 266 different human donors. The first, pan-cancer, cohort covers 25 high-grade to metastatic cancer types (8 cancer patients per type) and a control group (8 healthy donors). The validation cohort comprises additional plasma samples from ovarian, prostate and uterine cancer patients (12 per type) as well as additional samples from controls (22 new and 8 repeated). Samples were sequenced on a NovaSeq 6000 and are provided in FASTQ format.

  Dataset EGAD00001009713

• 2 paired WGS samples of peritumour regions of colorectal cancer

  2 paired WGS samples of peritumour regions of colorectal cancer (2 patients). The library was prepared using the Illumina TruSeq Nano FFPE kit and the sequencing was done on NovaSeq6000.

  Dataset EGAD00001009714

• PGDx elio™ plasma resolve assay: targeted sequencing analyses of WBC DNA

  The dataset "PGDx elio™ plasma resolve assay: targeted sequencing analyses of WBC DNA" includes paired end FASTQ reads of 49 white blood cell (WBC) genomic DNA samples from metastatic colorectal cancer patients. Sequencing was performed using a panel consisting of 33 genes, covering over 237,000 bp, targeting 25,000x depth across the targeted regions.

  Dataset EGAD00001009721

• PGDx elio plasma resolve analytical validation

  This dataset consists of 39 noncancerous donor and 62 cancer patient plasma samples (including 29 patients with CRC across a total of 13 tumor types) that were analyzed with the PGDx elio plasma resolve assay. The PGDx elio plasma resolve assay is a hybrid capture approach targeting 33 genes with sequencing performed using the Illumina NextSeq with 150bp paired-end reads. The bam files provided have been adapter masked and contain duplicate reads.

  Dataset EGAD00001009718

• PGDx elio™ plasma resolve assay: targeted sequencing analyses of plasma cfDNA

  The dataset "PGDx elio™ plasma resolve assay: targeted sequencing analyses of plasma cfDNA" includes paired end FASTQ reads of 183 cfDNA samples from metastatic colorectal cancer patients. Sequencing was performed using a panel consisting of 33 genes, covering over 237,000 bp, targeting 25,000x depth across the targeted regions.

  Dataset EGAD00001009719

• PGDx elio™ tissue complete assay: targeted sequencing analyses of tissue DNA

  The dataset "PGDx elio™ tissue complete assay: targeted sequencing analyses of tissue DNA" includes paired end FASTQ reads of 28 tissue samples from metastatic colorectal cancer patients. Sequencing was performed using a panel consisting of 505 genes, targeting 2,500x depth across the targeted regions.

  Dataset EGAD00001009720

• 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

• 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

• Normal sample for patient SA095

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

  Dataset EGAD00001009600

• 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

• 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

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__clear_cell_renal_cell_carcinoma

  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. 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.

  Study EGAS00001002608

• TCR β-chain repertoire characterization of regulatory and conventional T cells in peripheral blood from breast cancer patients and healthy individuals.

  Identification of tumor-specific effects on peripheral TCRβ repertoire formation in humans, investigation of the clonal origin of regulatory T cells in breast cancer patients and impact analysis of the tumor-specific conversion of conventional T cells into induced regulatory T cells on the peripheral Treg repertoire in humans.

  Study EGAS00001002699

• Preferential infiltration of distinct Vγ9δ2 T cells into Glioblastoma multiforme

  Glioblastoma multiforme (GBM), the most fatal CNS cancer, is highly infiltrative as a result of phenotypic alteration of cancer cells mainly driven by a mutual interaction among cancer cells, tumor-associated macrophages (TAMs) and other stromal cells. This work aims at characterizing GBM-infiltrating γδ T cells that may regulate the GBM tumor microenvironment and cancer cell gene expression. V(D)J repertoires of tumor-infiltrating and blood circulating γδ T cells from 4 patients were analyzed by NGS-based TCR sequencing. RNA gene expression profiling and immunostaining of cancer tissues were performed as well.

  Study EGAS00001002790

• Sensitive and Frequent Identification of High Avidity Neo-epitope Specific CD8 + T-cells in Immunotherapy-naïve Ovarian Cancer

  Immunotherapy directed against private tumor neo-antigens derived from non-synonymous somatic mutations is a promising strategy of personalized cancer immunotherapy. However, feasibility in low mutational load tumor types remains unknown. Comprehensive and deep analysis of circulating and tumor-infiltrating lymphocytes (TILs) for neo-epitope specific CD8 + T cells allowed prompt identification of oligoclonal and polyfunctional such cells from most immunotherapy-naïve patients with advanced epithelial ovarian cancer studied. Neo-epitope recognition was discordant between circulating T cells and TILs, and was more likely to be found among TILs, which displayed higher functionalavidity and unique TCRs with higher predicted affinity than their blood counterparts. Our results imply that identification of neo-epitope specific CD8 + T cells is achievable even in tumors with relatively low number of somatic mutations, and neo-epitope validation in TILs extends opportunities for mutanome-based personalized immunotherapies to such tumors.

  Study EGAS00001002803

• “Castration-persistence” is a distinct state of tolerance to androgen receptor targeting therapies in prostate cancer

  Androgen receptor (AR) signalling is important in prostate cancer progression, and therapies that specifically target this pathway are the mainstay of treatment for advanced disease. Treatment however is non-curative, and resistance develops inevitably with time. Although the mechanisms that drive progressive ‘castration resistant’ disease are reasonably well characterized, how tumours survive acute pathway inhibition is unclear. We performed a neo-adjuvant study of a novel combination of AR targeting therapies, and noted that the objective response to treatment was highly variable. To determine what was driving tumour persistence in poorly responding patients, we comprehensively characterised pre- and post-treatment samples were using both whole genome and RNA-sequencing. We find that ‘castration-persistence’ is a distinct state from ‘castration-resistance’, and is mediated by global transcriptional reprogramming leading to transitional EMT state, which is shared with benign luminal epithelial cells. This appears to be AP-1 and KLF driven, and represents and integration of multiple signalling pathways, particularly IGF2, offering a number of tractable strategies to improve clinical response to AR targeting therapies.

  Study EGAS00001003172

• Genome-wide cell-free DNA fragmentation in patients with cancer

  Cell-free DNA (cfDNA) in the blood provides a noninvasive diagnostic avenue for patients with cancer. However, characteristics of the origins and molecular features of cfDNA are poorly understood. We developed an approach to evaluate fragmentation patterns of cfDNA across the genome and found that cfDNA profiles of healthy individuals reflected nucleosomal patterns of white blood cells, while patients with cancer had altered fragmentation profiles. We applied this method to analyze fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers and 245 healthy individuals. A machine learning model incorporating genome-wide fragmentation features had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity, with an overall AUC of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation based cfDNA analyses detected 91% of cancer patients. The results of these analyses highlight important properties of cfDNA and provide a proof of principle approach for screening, early detection, and monitoring of human cancer.

  Study EGAS00001003611

• Lindsey E. Jones et al. Patient-derived cells from recurrent tumors that model the evolution of IDH-mutant glioma. Neuro-Oncology Advances, 2020. We established robust, versatile, and well-characterized Patient Derived Cells (PDC) of an IDH1-mutant astrocytoma and an IDH1-mutant oligodendroglioma that represents defined evolutionary stages of chemotherapy-induced hypermutation. The PDCs retain tumor subtype defining features over time as well as classic hallmarks of cancer, including anchorage independent growth and cell immortality. The integrated phylogenies composed of PDCs,single-cell-derived PDCs, patient-derived xenografts, and corresponding spatiotemporal tumor tissue samples also provide new insight into the clonality, evolutionary pattern, and immense mutational load of hypermutated IDH mutant gliomas.

  IDH-mutant lower-grade gliomas can undergo malignant progression via temozolomide-driven hypermutation. Patient-derived cells (PDC) that model the genetically distinct hypermutated (HM) tumor subgroup are generally lacking, and few if any human brain tumor cell models are from defined evolutionary time points. Here, we characterize multiple PDC derived from independent surgical specimens of IDH1-mutant recurrences, including an ATRX and TP53-mutant astrocytoma and a 1p/19q co-deleted and TERT promoter-mutant oligodendroglioma. We determined the evolutionary time points represented by each PDC using exome sequencing and phylogenetic reconstruction, comparing the PDC and single cell clones of the PDC (scPDC) to multiple spatiotemporal tumor tissue samples, PDC-derived xenografts (PDX) and patient-matched blood. The tumor samples exhibited TMZ-induced mutagenesis and a branching pattern of evolution. We found clear evidence of two fully independent founder HM clones in the tumor tissue that are faithfully represented by independent PDC. The PDC, scPDC and PDX also shared the mutagenesis signature and represent the mid and later evolutionary time points of their corresponding tumors. The PDC maintained the tumor subtype-defining features over many passages, including heterozygosity of the IDH1 R132H mutation, production of 2-hydroxyglutarate (2-HG), and subtype-specific telomere maintenance mechanisms. The PDC from both tumors exhibited anchorage-independent growth in soft agar. The oligodendroglioma PDC formed infiltrative intracranial tumors with characteristic oligodendroglioma histology, initially with a long period to tumor formation. We conclude that the PDC, scPDC and PDX faithfully model the heterogeneous clonal origins of the corresponding tumor tissue. The multilevel analysis also provides new insight into the intratumoral heterogeneity and vast mutational load of HM glioma. The PDC from multiple evolutionary time points presented in the context of full clinical timelines may be useful to model evolution and intratumoral heterogeneity, important sources of therapeutic failure.

  Study EGAS00001003992

• Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia

  Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We therefore investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population, with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of the cell state accounts for a significant component of bottleneck selection during induction chemotherapy.

  Study EGAS00001004407

• FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (RNA-seq data)

  PIK3CA, encoding the PI3Kα isoform, is the most frequently mutated oncogene in estrogen receptor-positive breast cancer. Isoform-selective PI3K inhibitors are used clinically but intrinsic and acquired resistance limits their utility. Improved selection of patients that will benefit from these drugs requires novel predictive biomarkers. We show here that persistent FOXM1 expression following drug treatment is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer. Since the transcription factor drives expression of lactate dehydrogenase (LDH) but not hexokinase 2 (HK-II), changes in FOXM1 expression can be detected using magnetic resonance imaging of LDH-catalyzed hyperpolarized 13C label exchange between pyruvate and lactate but not by PET measurements of HK-II-mediated trapping of the glucose analog 2-deoxy-2-[18F]fluorodeoxyglucose.

  Study EGAS00001004452