6051 results for "canc*"

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• Angiopredict: predicting response for bevacizumab treatment

  In ANGIOPREDICT, academic cancer biologists and industry-based biotechnology researchers worked with clinicians to identify biomarkers to predict whether individual metastatic colorectal cancer patients will respond positively to bevacizumab containing therapy. In this retrospective study, copy number profiles were determined using whole genome shallow sequencing, and progression free survival and treatment data was collected.

  Study EGAS00001002724

• Shallow-whole genome sequencing for copy numbers in resectable gastric cancer treated with surgery alone

  Shallow-whole genome sequencing for copy numbers in resectable gastric cancer treated with surgery alone

  Dataset EGAD00001011994

• ICARUS-BREAST01-ExomeSeq

  Efficacy, safety and biomarker analysis of ICARUS-BREAST01: a phase 2 Study of Patritumab Deruxtecan in patients with HR+/HER2- advanced breast cancer. Whole-exome sequencing (WES) is available for 43 tumor samples and 43 blood samples collected at entry into the trial.

  Study EGAS50000000542

• ICARUS-BREAST01-RNAseq

  Efficacy, safety and biomarker analysis of ICARUS-BREAST01: a phase 2 Study of Patritumab Deruxtecan in patients with HR+/HER2- advanced breast cancer. RNA-squencing is available for 44 samples comprising 22 samples collected at entry into the trial and 22 samples collected during treatment (cycle 1 day 3, cycle 1 day 19, or cycle 2 day 3) from 22 patients.

  Study EGAS50000000543

• ICARUS-BREAST01 Dataset

  A phase II clinical trial (NCT04965766) of patritumab deruxtecan in 99 breast cancer patients. Whole-exome sequencing (WES) is available for 43 tumor samples and 43 blood samples collected at entry into the trial. RNA-squencing is available for 44 samples comprising 22 samples collected at entry into the trial and 22 samples collected during treatment (cycle 1 day 3, cycle 1 day 19, or cycle 2 day 3) from 22 patients.

  Dataset EGAD50000000773

• RNA-seq of CRC patient-derived xenograft tumors

  We developed a novel antibody-drug conjugate (ADC), 84-EBET. To analyze mechanism of action of 84-EBET, we obtained bulk RNA-seq data of the patient-derived xenograft (PDX) model derived from colorectal cancer (CRC) patient. The PDX model was treated with 5-fluorouracil + oxaliplatin, 84-EBET or vehicle, and then gene expression changes were analyzed with bulk RNA-seq.

  Study EGAS50000000598

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

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

  Dataset EGAD00001005763

• Genomic profiling of Rare Tumors Release 2

  Utilizing the rare and unique patient samples provided by Pattern Medicine, LLC, MD Anderson will molecularly characterize rare cancer samples, and potentially generate a collection of preclinical models to enable identification of novel therapeutic targets and concepts, as well as evaluation of innovative treatment strategies.

  Study EGAS50000000615

• Acetalax and bisacodyl triple-negative breast cancer RNA-Seq

  Analysis of gene expression in triple negative breast cancer xenografts exhibiting good or bad response upon acetalax and bisacodyl treament.

  Study EGAS50000000618

• Luminal breast epithelial cells from wildtype and BRCA mutation carriers harbor copy number alterations commonly associated with breast cancer

  Cancer-associated mutations have been documented in normal tissues, but the prevalence and nature of somatic copy number alterations and their role in tumor initiation and evolution is not well understood. Here, using single cell DNA sequencing, we describe the landscape of CNA variants in >48,000 breast epithelial cells from women with normal or high risk of developing breast cancer. Individual cells with one or two of a specific subset of CNAs (e.g. 1q gain and 16q, 22q, 7q, and 10q loss) are detectable in almost all breast tissues and, in those from BRCA1 or BRCA2 mutations carriers, occurs prior to loss of heterozygosity (LOH) of the wildtype alleles. These CNAs, which are among the most common associated with ductal carcinoma in situ (DCIS) and malignant breast tumors, are enriched almost exclusively in luminal cells and are generally absent from basal myoepithelial cells. Allele-specific analysis of the enriched CNAs reveals that each allele was independently altered, demonstrating convergent evolution of these CNAs in an individual breast. Tissues from BRCA1 or BRCA2 mutation carriers contained a small percentage of cells with extreme aneuploidy, featuring loss of TP53, LOH of BRCA1 or BRCA2, and multiple breast cancer-associated CNAs in addition to one or more of the common CNAs in 1q, 10q or 16q. Notably, cells with intermediate levels of CNAs were not detected, arguing against a stepwise gradual accumulation of CNAs. Overall, our findings demonstrate that chromosomal alterations in normal breast epithelium including from high risk women, link early stage CNAs to clonally expanded tumor genomes .

  Study EGAS00001007716

• Comparison of good PDX responders and bad PDX responders

  RNA-Seq raw data of untreated breast cancer xenografts known as good or bad acetalax responders.

  Dataset EGAD50000000876

• Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

  The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1,070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8,887 TCGA, and 1,261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n=1,261) including our lung cancer cohort (n=497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

  Study EGAS50000000324

• Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

  Raw bam file for bulk RNA-seq of checkpoint-blockade treated lung cancer cohorts

  Dataset EGAD50000000469

• DAC for a single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  Nodal B-cell non-Hodgkin lymphomas (B-NHL) are a clinically diverse set of cancers thought to originate from distinct stages of B-cell maturation. To characterize intratumor heterogeneity across B-NHL entities, we performed CITE-Seq (cellular indexing of transcriptomes and epitopes) on 51 lymph node samples including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL) and marginal zone lymphoma (MZL, n = 11) tumors, in addition to non-malignant reactive lymph nodes (rLN, n = 8). We also performed 5 prime scRNA-seq with immune receptor profiling for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL, and 2 MZL).

  Dac EGAC50000000226

• Genomic Alterations and Transcriptional Phenotypes in Circulating Tumor DNA and Matched Metastatic Tumor

  We analyzed a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent small cell lung cancer. We conducted cfDNA low-pass whole genome sequencing (0.1X coverage) and exome sequencing (130X coverage) in comparison with time-point matched tumor characterized using whole-exome (130X) and transcriptome sequencing. A direct comparison of cfDNA and tumor biopsy revealed that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor, but also identifies clinically relevant resistance mechanisms and cancer driver alterations not detected in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Sequencing coverage of plasma DNA fragments around transcription start sites showed distinct treatment-related changes and captured the expression of key transcription factors n the corresponding SCLC tumors.

  Study phs003689

• BPH Tissues for Cell Culture and Analysis - A Patient-Derived Xenograft Model Using Benign Prostatic Tissues

  Our goal was to establish and characterize a patient-derived xenograft model for benign prostatic hyperplasia (BPH) using human benign prostatic tissues. Characterization of the model was done by histology, immunohistochemistry, proteomics, and transcriptome analysis (RNAseq). The RNAseq data will be deposited in dbGaP. Subjects were undergoing prostatectomy for prostate or bladder cancer, with concurrent BPH, and consented to the use of surplus tissue for research studies. The RNAseq studies included 6 patients. Phenotypic data include patient age, sex, prostate size, serum prostate-specific antigen (PSA) level, and international prostate symptom score (IPSS). Sequencing was done on an Illumina NovaSeq 6000, and data were analyzed using Kallisto. Data are submitted as raw sequencing (fastq) files.

  Study phs003692

• Patient-Derived Breast Cancer Organoid Study

  This study uses normal and tumor tissues from breast cancer patients to derive 3D cell culture models called organoids. Organoid technology is an innovative platform that can be used to study a patient's tumor in a dish ex-vivo. As part of this study we have comprehensively characterized these models using next-generation sequencing for: 1) pathogenic SNVs in a targeted gene panel 2) copy number alterations 3) transcriptome analysis using RNA sequencing and 4) single-cell RNA sequencing

  Study phs002722

• Epigenomics of Prostate Cancer from Cell Free Plasma

  We sought to perform epigenomic characterization of prostate cancers by profiling circulating nucleosomes isolated from patient plasma using cell free chromatin immunoprecipitation followed by sequencing (cfChIP-seq). Our major findings include identification of prostate-cancer specific regulatory elements using a novel statistical method accounting for variable tumor DNA fraction in plasma, inferring transcription factor activity based on binding motif enrichment within regulatory elements, and epigenetically phenotyping patients based on clinical and genomic features. Data available in dbGaP will include cfChIP-seq from approximately 50 patients with a known diagnosis of cancer and healthy patient controls.

  Study phs003482

• Prostate Cancer Upgrading Reference Set

  This study aimed to investigate clinical, demographic, and molecular features of men diagnosed with low-risk prostate cancer [ISUP Grade Group 1 (GG1)] at biopsy who underwent radical prostatectomy, and for whom an increased tumor grade (ISUP Grade Group 2 or above) was subsequently detected in the resected prostate. This phenomenon is termed upgrading. In collaboration with the Early Detection Research Network (EDRN), a blinded reference set was established to facilitate upgrading biomarker detection and validation. A prospective, multi-institutional, cohort of men with the GG1 prostate cancer and intention to undergo prostatectomy was recruited. Biospecimens (blood, urine) were collected and this biorepository comprises the official EDRN Upgrading Reference Set (URS). Recording of case-control status (case = upgrading at prostatectomy, control = no upgrading at prostatectomy) was coordinated by the Data Management and Coordinating Center (DMCC) of the EDRN, who ensured blinding of all participating researchers. A subset of the reference set (n = 192) was chosen to undergo whole genome sequencing (WGS) for the investigation of a germline genomic biomarker of upgrading. Variant calls from all sequenced URS whole genomes are available through dbGaP, with the exception of one sample for which sequencing was not successful. Among investigated clinical features, prostate-specific antigen (PSA) density and percent of cancer in pre-prostatectomy positive biopsy cores were significantly associated with upgrading. Two polygenic risk score-based biomarkers were computed in the genetic sub-cohort and tested against case-control status. Neither was predictive of upgrading.

  Study phs003670

• Whole Exome and RNA Sequencing of 22 Patient-Derived Xenografts from Estrogen Receptor-Positive Breast Cancers

  We performed RNA and whole exome sequencing on 22 patient-derived xenografts (PDX) from estrogen receptor-positive (ER+) breast cancers, in the presence and absence of estradiol (E2) supplementation. Exome coverage across more than 3,500 clinically relevant genes that previously were covered below 20X (~2.72Mb) was achieved. On average, 11.2Gb of unique sequence data were generated with 95.3% of the bases in the exome design covered to 20x read depth or greater. RNA sequencing was generated from xenograft tumors. Between 50 and 85M total reads were performed. The average strand-specificity and rRNA rate was 98% and 1.5%, respectively. Transcripts for 17,000 to 27,087 genes were detected in these samples.

  Study phs003324

• NCI Cancer Model Development for the Human Cancer Model Initiative (HCMI)

  The Cancer Model Development Center (CMDC) project is part of the Human Cancer Model Initiative (HCMI) and is managed by the Office of Cancer Genomics within the Center for Cancer Genomics at the National Cancer Institute (NCI). The goal of the HCMI is to generate approximately 1000 novel human "next generation" tumor-derived models as a community resource. The models aim to faithfully recapitulate the cellular complexity and heterogeneity of human tumors. The CMDCs are tasked by the NCI with producing a proportion of the cancer models. Wellcome Sanger Institute (WSI) is a contributing member of the HCMI. Models developed by HCMI are being derived from a number of tumor types and subtypes (see below), including rare adult, pediatric and understudied cancers. The number of models and the clinical sites from which patient samples are acquired is increasing. Normal tissue, originating tumor, and tumor-derived models are sequenced. The next generation cancer models are associated with rich clinical and genomic data through the NCI Genomic Data Commons for all researchers to advance cancer research. The CMDC and HCMI models will serve as excellent tools in research to provide insight into the pathways that influence tumor growth and progression, how tumors respond to therapy, etc. Ultimately, the data generated using these models will support the development of personalized oncology. There is a delay between registered IDs and model availability. The current model count is found in The HCMI Searchable Catalog which is updated when new models are available for distribution to the research community.

  Study phs001486

• IMPRESS: Improved methylation profiling using restriction enzymes and smMIP sequencing, combined with a new biomarker panel, creating a multi-cancer detection assay

  Background Despite the worldwide progress in cancer diagnostics, more sensitive diagnostic biomarkers are needed. The methylome has been extensively investigated in the last decades, but a low-cost, bisulfite-free detection method for multiplex analysis is still lacking. Methods We developed a methylation detection technique called IMPRESS, which combines methylation-sensitive restriction enzymes and single-molecule Molecular Inversion Probes. We used this technique for the development of a multi-cancer detection assay for eight of the most lethal cancer types worldwide. We selected 1791 CpG sites that can distinguish tumor from normal tissue based on DNA methylation. These sites were analysed with IMPRESS in 35 blood, 111 tumor and 114 normal samples. Finally, a classifier model was built. Results We present the successful development of IMPRESS and validated it with ddPCR. The final classifier model discriminating tumor from normal samples was built with 358 CpG target sites and reached a sensitivity of 0.95 and a specificity of 0.91. Moreover, we provide data that highlight IMPRESS’s potential for liquid biopsies. Conclusions We successfully created an innovative DNA methylation detection technique. By combining this method with a new multi-cancer biomarker panel, we developed a sensitive and specific multi-cancer assay, with potential use in liquid biopsies.

  Study EGAS50000000624

• Tumor Profiler Melanoma Study

  The Tumor Profiler Study is an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease. Technologies used are digital pathology, targeted NGS, single-cell RNA, single-cell DNA, bulk RNA, bulk proteotyping, single-cell CyTOF, imaging mass cytometry, pharmacoscopy, 4i Drug Response Profiling, and cell-free DNA. These technologies were applied to 240 tumor samples collected over 3 years across three cancer indications: metastatic melanoma, metastatic epithelial ovarian cancer, and acute myeloid leukemia (AML). Here, we publish the scRNA, scDNA, and bkRNA data of the melanoma cohort.

  Study EGAS50000000599

• BIOMIROX

  This dataset regroups RNAseq of paired primary colorectal cancers and liver metastases extracted from 113 patients In Paris and Besançon University Hospitals. 216 FASTQ files from single-end 3' PolyA (QuantSeq 3′mRNA-Seq Kit FWD for Illumina (Lexogen)) RNAseq samples were generated On NOVASEQ 6000 (ILLUMINA).

  Dataset EGAD50000000443

• EPICC: Evolutionary Predictions in Colorectal Cancer WGS-3

  Primary tumour tissue and matched normal samples were prospectively collected from patients undergoing curatively-intentioned surgery at University College London Hospital (UCLH). All patients gave informed consent for collection of their materials to the UCLH Cancer Biobank (REC approval 15/YH/0311). Four regions of each primary cancer were sampled by punch biopsy or scalpel dissection, at 12, 3, 6 and 9 o’clock positions around the tumour periphery. Tissue pieces were manually dissociated under the microscope using two 16 G needles, where individual glands were pulled away from the tissue mass. Sequencing and mutation calling DNA fractions were extracted using the Zymo QuickDNA Microprep plus kit according to the manufacturer’s instructions. Only samples with a total DNA yield higher than 10 ng were taken forward for WGS library preparation. Libraries were prepared using the NEBnext Ultra II FS kit according to manufacturer’s instructions. Samples with sufficient library DNA yield and characteristic fragment size distribution (~200-500 bp) were further subjected to deep (~35x coverage) WGS. Sequence libraries were multiplexed and sequenced on an Illumina Novaseq.

  Dataset EGAD00001008642