7411 results for "canc*"

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• Bulk RNAseq of cancer-associated CD8 T cell populations

  Bulk mRNA sequencing of cancer-associated CD8 T cell populations isolated from tumor and tumor-adjacent tissue from non-small cell lung cancer patients. CD8 T cell populations isolated were CD103+CD39- and CD103+CD39+ from tumor tissue, and CD103+CD39- from adjacent tissue. These populations were isolated from 5 patients, totaling 15 samples. bulk poly-A RNA was isolated and libraries were prepped using the Smart-Seq2 protocol, followed by paired-end sequencing (Illumina). Raw data are in fastq format.

  Dataset EGAD50000002740

• GBM-Space: Whole Genome Sequencing of Laser Capture Microdissected Glioblastoma (LCM-WGS)

  Cancer cells display heterogeneous and dynamic states in glioblastoma, but how these malignant states arise and whether they follow a tractable cellular trajectory across tumours is poorly understood. Here, we generate a deep single cell and spatial multi-region atlas of 12 isocitrate dehydrogenase wild-type (IDH-wt) primary glioblastomas that integrates transcriptomic, epigenomic and genomic analysis to comprehensively characterise their tumour heterogeneity. This dataset contains whole genome sequencing (WGS) data generated from regions of interest (ROIs) collected via laser capture microdissection (LCM), towards identifying copy number alterations and subclones directly at the DNA level.

  Dataset EGAD00001016155

• Dataset for 18 PTK7 RNASeq samples

  This dataset contains 18 RNASeq tumor and control samples of patient with colorectal cancer. Sequencing was performed on Illumina NovaSeq X Plus. The sequencing was always paired.

  Dataset EGAD00001016160

• Single cell characterization of T-cell lymphoma: RNA (2025-07-31)

  Lymphomas are types of blood cancer derived from lymphocytes, and can be classified into two main categories, Hodgkin's lymphomas (HL) and non-Hodgkin lymphomas (NHL). T-cell lymphoma is a type of NHL that develops from T lymphocytes and accounts for 10-15% of total NHL cases. As a rare disease, T-cell lymphoma is functionally, pathologically, and clinically complex and heterogeneous. The 2016 revision of the WHO classification has listed 29 subtypes of mature T/NK-cell neoplasms, among which over 25 are T lymphocyte in origin. These subtypes of T-cell lymphomas have different biological behaviours and clinical prognosis. Clinically, T-cell lymphoma ranges from indolent (slow-growing) to aggressive (fast-growing and spreading) disease. According to the guideline booklet provided by the Lymphoma Research Foundation (https://lymphoma.org/aboutlymphoma/nhl/), indolent T-cell lymphoma subtypes include adult T-cell leukaemia/lymphoma (ATLL), cutaneous T-cell lymphoma (CTCL), and mycosis fungoides (MF), while the aggressive subtypes include anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphoma (PTCL), and T lymphoblastic lymphoma (TLBL). PTCL and ALCL represent the most common subtypes of aggressive T-cell lymphoma whereas TLBL is a rare and aggressive subtype mostly diagnosed in children. In this study, we will use single cell RNA sequencing and single cell multiomic methods three aggressive T-cell lymphoma subtypes (ALCL, PTCL and TLBL) and try to gain a deeper insight into the genotypic and phenotypic features of these T-cell lymphomas. . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015669

• A non-canonical lymphoblast in refractory childhood T-cell leukaemia

  Refractory cancers may arise either through the acquisition of resistance mechanisms or represent distinct disease states. The origin of childhood T-cell acute lymphoblastic leukaemia (T-ALL) that does not respond to initial treatment, i.e. refractory disease, is unknown. Refractory T-ALL carries a poor prognosis and cannot be predicted at diagnosis. Here, we perform single cell mRNA sequencing of T-ALL from 58 children (84 samples) who did, or did not respond to initial treatment. We identify a transcriptionally distinctive blast population, exhibiting features of innate-like lymphocytes, as the major source of refractory disease. Evidence of such blasts at diagnosis heralds refractory disease across independent datasets and is associated with survival in a large, contemporary trial cohort. Our findings portray refractory T-ALL as a distinct disease with the potential for immediate clinical utility. This dataset contains 56 single-cell RNA sequencing data files with corresponding single-cell T cell receptor sequencing data files. The data here form the validation cohort in our paper.

  Dataset EGAD00001015673

• Colorectal Microenvironment Spatial Mapping Dataset

  Two samples derived from a colorectal cancer female patient - tumor tissue and normal adjacent tissue analyzed via Visium Spatial Gene Expression.

  Dataset EGAD00001015675

• Additional RNAseq data of metastatic breast cancer samples

  Paired-end RNA sequencing data from 33 metastatic breast cancer samples. Sequencing was performed on the Illumina NovSeq 6000 using NEBNext Single Cell and Low Input RNA Library Prep Kit for Illumina.

  Dataset EGAD00001015678

• Whole exome sequencing data for paired non-serous endometrial and ovarian carcinomas from 27 patients with concurrent tumours.

  Dataset for Synchronous Endometrial and Ovarian Cancer

  Dataset EGAD00001015680

• Whole genome sequencing of tumor samples from advanced pre-treated NSCLC patients recruited in a phase I/II single-arm trial utilising CVA21, an oncolytic coxsackie virus, in combination with pembrolizumab.

  This dataset contains paired tumor and matched normal whole-genome sequencing (WGS) data from longitudinally collected samples of patients enrolled in an Australian clinical study involving multiple cancer types. For each patient, one or more tumor biopsies were obtained at different treatment time points (Scr, Day 0, Day 15, Day 22, Day 33), and matched normal samples were collected either from blood or non-tumor tissue.

  Dataset EGAD00001015682

• Genomic Sequencing of Triple Negative Breast Cancer - CUTseq data

  CUTseq data for study EGAS00001008261

  Dataset EGAD00001015684

• Genomic Sequencing of Triple Negative Breast Cancer - Exome data

  Exome data for study EGAS00001008261

  Dataset EGAD00001015687

• Genomic and Functional Profiling of Acral Melanoma from the admixed Brazilian Population Reveals Disease Drivers and Targetable Vulnerabilities

  Acral melanoma, a subtype of melanoma characterized by its occurrence on palms, soles, and beneath the nails, presents unique challenges in diagnosis and treatment due to its distinct genetic and environmental factors. Despite recent advancements in targeted therapies for melanoma, the efficacy of treatments in acral melanoma remains limited, emphasizing the urgent need for a deeper understanding of its genetic landscape. CRISPR-based functional genomics screening has emerged as a powerful tool for uncovering genetic dependencies and potential therapeutic targets in various cancers. In this project, we present the results of CRISPR screening performed on acral melanoma cell lines to identify genes essential for cell survival and proliferation collectively termed “fitness". Through this screening approach, we aim to elucidate the molecular mechanisms underlying the pathogenesis of acral melanoma and identify novel therapeutic vulnerabilities. Our findings reveal key genetic drivers and pathways implicated in acral melanoma progression, offering potential targets for the development of precision therapies tailored to this aggressive subtype of melanoma

  Dataset EGAD00001015690

• Dataset for "CutNRun" sequencing of 12 samples of patients with colorectal cancer

  This dataset contains 12 "CutNRun" sequencing samples of patients with colorectal cancer. The sequencing was performed on Illumina NextSeq 550 using Cell Signaling TechnologyDNA Library Prep _Kit for Illumina Systems. The sequencing was always paired.

  Dataset EGAD00001015694

• PEACE (Posthumous Evaluation of Advanced Cancer Environment) whole exome sequencing data

  PEACE (Posthumous Evaluation of Advanced Cancer Environment) whole exome sequencing data

  Dataset EGAD00001015763

• Ancestry and somatic profile predict acral melanoma origin and prognosis – WXS

  Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

  Dataset EGAD00001015755

• Ancestry and somatic profile predict acral melanoma origin and prognosis – RNA

  Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

  Dataset EGAD00001015756

• Genomics Define Malignant Transformation in Myeloma Precursor Conditions

  Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

  Dataset EGAD00001015768

• Single-cell RNA sequencing of breast cancer lung metastasis and adjacent normal tissue

  This dataset contains single-cell RNA sequencing data from two samples: L1 (breast cancer metastasis to lung) and N3 (adjacent normal lung tissue) from a female patient. The samples were processed using 10X Chromium 3' technology and sequenced on Illumina NextSeq 500. This data supports the study "Endocrine therapy reprogramming of breast cancer facilitates metastatic escape via upregulation of P-Rex1/Rac1 signalling" which investigates how endocrine therapy reprograms breast cancer cells, leading to upregulation of P-Rex1/Rac1 signalling pathways that facilitate metastatic escape. The single-cell analysis reveals cellular reprogramming mechanisms in endocrine therapy-resistant breast cancer cells that form metastases in the lung.

  Dataset EGAD00001015769

• Resolution of tumour cell populations enhances specificity of treatment options for precision cancer medicine

  This dataset comprises FASTQ files for single-cell RNA-seq and single-cell ATAC-seq data. These data were generated using 10x Genomics' multiome kit. In this cohort, there are 25 samples from 17 human cancer patients.

  Dataset EGAD00001015782

• MethylScan data of plasma samples

  10ng from each cfDNA sample was used as the input material. For gDNA samples, 1.5-2ug each was first sonicated by Covaris R230 system (Woburn, MA). Next, size-selection with 0.8x-1.5x two-step Ampure XP beads was performed to enrich the 150-200bp fragments. 200ng of the size-selected gDNA was used as input material. The pre-capture library construction was performed with NEB UltraII DNA library prep kit for Illumina (Ipswich, MA) and the adapter was the methylated EM-seq adapter. Before the first PCR amplification, the gDNA samples were subjected to bisulfite conversion by QIAGEN Epitect kit, and cfDNA samples were subjected to enzymatic conversion by NEBNext enzymatic methyl-seq kit if the hybrid capture panel designed for converted DNA was used downstream. Twist panel hybrid capture was performed with their standard protocol (Twist Bioscience, South San Francisco, CA). The concentration of the post-capture library after the second PCR amplification was measured by Qubit 1xdsDNA HS kit (ThermoFisher, Waltham, MA). Their quality was examined by TBE-UREA PAGE and Bioanalyzer before sequencing. Libraries were then sequenced with 150bp paired-end reads on Illumina machines by Genewiz, Inc. (South Plainfield, NJ, USA). This dataset contains MethylScan data of the plasma samples from 248 noncancer individuals and 27 cancer patients (17 liver and 10 GI-tract cancers).

  Dataset EGAD00001015815

• Genome and transcriptome sequence data from a recurrent odontogenic ghost cell carcinoma of the left maxilla patient

  Genome and transcriptome sequence data from a recurrent odontogenic ghost cell carcinoma of the left maxilla patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001658

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

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

  Dataset EGAD00001001876

• Genome and transcriptome sequence data from a breast ductal carcinoma patient

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

  Dataset EGAD00001002047

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001002030

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001002534

• Genome and transcriptome sequence data from a ovarian cancer patient

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

  Dataset EGAD00001002544

• Genome and transcriptome sequence data from a melanoma patient

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

  Dataset EGAD00001002546

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001002536

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

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

  Dataset EGAD00001002587

• Genome and transcriptome sequence data from a retroperitoneal leiomyosarcoma patient

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

  Dataset EGAD00001002583

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001002576

• Genome and transcriptome sequence data from a primary unknown patient

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

  Dataset EGAD00001002569

• Genome and transcriptome sequence data from a adenocarcinoma of the lung patient

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

  Dataset EGAD00001002564

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

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

  Dataset EGAD00001002589

• Genome and transcriptome sequence data from a neuroendocrine tumor likely pancreatic origin patient

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

  Dataset EGAD00001002591

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

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

  Dataset EGAD00001002597

• Genome and transcriptome sequence data from a medullary thyroid carcinoma patient

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

  Dataset EGAD00001002599

• Genome and transcriptome sequence data from a pancreatic cancer (likely pnet) patient

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

  Dataset EGAD00001002607

• Genome and transcriptome sequence data from a adenocarcinoma of unknown primary patient

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

  Dataset EGAD00001002611

• Genome and transcriptome sequence data from a metastatic lymphoepithelial-like thymic carcinoma patient

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

  Dataset EGAD00001002614

• Genome and transcriptome sequence data from a serous endometrial cancer patient

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

  Dataset EGAD00001002631

• Genome and transcriptome sequence data from a squamous cell carcinoma of unknown primary patient

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

  Dataset EGAD00001002624

• Genome and transcriptome sequence data from a small cell/neuroendocrine carcinoma of the lung patient

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

  Dataset EGAD00001002617

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

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

  Dataset EGAD00001004903

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

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

  Dataset EGAD00001004912

• Genome and transcriptome sequence data from a metastatic neuroendocrine tumor, lung primary patient

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

  Dataset EGAD00001004924

• 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 EGAD00001004928

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

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

  Dataset EGAD00001004930

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

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

  Dataset EGAD00001005763

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

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

  Dataset EGAD00001005835