3740 results for "*oma"

in 39.19 milliseconds.

• Genomic evolution and transcriptional changes in the evolution of prostate cancer into neuroendocrine and ductal carcinoma types (RNAseq)

  Prostate cancer is typically of acinar adenocarcinoma type but can occasionally present as neuro-endocrine and/or ductal type carcinoma. These are associated with clinically aggressive disease and the former often arises on a background of androgen deprivation therapy, although it can al-so arise de novo. Two prostate cancer cases were sequenced by exome capture from archival tis-sue. Case 1 was de novo small cell neuroendocrine carcinoma and ductal adenocarcinoma with 3 longitudinal samples over 5 years. Case 2 was a single time point after development of treatment related neuroendocrine prostate carcinoma. Case 1 showed whole genome doubling in all sam-ples and focal amplification of AR in all samples except the first time point. Phylogenetic analysis revealed a common ancestry for the ductal and small cell carcinoma. Case 2 showed 13q loss (involving RB1) in both adenocarcinoma and small cell carcinoma regions, and 3p gain, 4p loss, 17p loss (involving TP53) in the latter. By using highly curated samples, we demonstrate for the first time that small cell neuroendocrine and ductal prostatic carcinoma can have a common an-cestry and the process of evolution over time. We highlight whole genome doubling in a patient with prostate cancer relapse, reinforcing its poor prognostic nature.

  Study EGAS00001007428

• scRNA-seq of LN and lymphoma stroma

  The activities of non-haematopoietic cells (NHCs), including mesenchymal stromal cells and endothelial cells, in lymphomas, reportedly underlie lymphomagenesis. However, understanding of lymphoma NHCs has been hampered by unexplained NHC heterogeneity, even in normal human lymph nodes (LNs). Here, we constructed a single-cell transcriptome atlas of >100,000 NHCs collected from 27 human samples, including LNs and various nodal lymphomas, revealing 30 distinct subclusters, including some that were previously unrecognized. Notably, this atlas was highly useful for comparative analysis with lymphoma NHCs, which revealed an unanticipated landscape of subcluster-specific changes in gene expression and interaction with malignant cells in follicular lymphoma NHCs, facilitating our understanding of stromal remodelling in lymphoma and potentially suggesting novel biomarkers. Our study largely updates NHC taxonomy in human LNs and pertains to analysis of disease status, providing a rich resource and deeper insights into LN and lymphoma biology and offering to advance lymphoma management and therapy.

  Study EGAS00001005732

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

  The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p < 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at >1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater clinical implementation.

  Study EGAS00001005738

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

  The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p < 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at >1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater clinical implementation.

  Study EGAS00001005747

• Analysis of enhancer-promoter connectivity alterations

  The study uncovers epigenomic changes associated with dexamethasone response heterogeneity in myeloma cells, revealing rewired promoter-enhancer interactions and DNA loop stabilization

  Study EGAS00001007411

• Genomic evolution and transcriptional changes in the evolution of prostate cancer into neuroendocrine and ductal carcinoma types

  Prostate cancer is typically of acinar adenocarcinoma type but can occasionally present as neuro-endocrine and/or ductal type carcinoma. These are associated with clinically aggressive disease and the former often arises on a background of androgen deprivation therapy, although it can al-so arise de novo. Two prostate cancer cases were sequenced by exome capture from archival tis-sue. Case 1 was de novo small cell neuroendocrine carcinoma and ductal adenocarcinoma with 3 longitudinal samples over 5 years. Case 2 was a single time point after development of treatment related neuroendocrine prostate carcinoma. Case 1 showed whole genome doubling in all sam-ples and focal amplification of AR in all samples except the first time point. Phylogenetic analysis revealed a common ancestry for the ductal and small cell carcinoma. Case 2 showed 13q loss (involving RB1) in both adenocarcinoma and small cell carcinoma regions, and 3p gain, 4p loss, 17p loss (involving TP53) in the latter. By using highly curated samples, we demonstrate for the first time that small cell neuroendocrine and ductal prostatic carcinoma can have a common an-cestry and the process of evolution over time. We highlight whole genome doubling in a patient with prostate cancer relapse, reinforcing its poor prognostic nature.

  Study EGAS00001007412

• WGS and WES of pediatric osteosarcoma

  Despite being the most common childhood bone tumor, the genomic characterization of osteosarcoma remains incomplete. Very few metastatic cases have been sequenced to date, a required step to better understand the progression and evolution of this tumor. Here, we present an integrative analysis of whole genome and whole exome sequencing (WGS and WES) of a cohort of primary and metastatic pediatric osteosarcoma, in order to identify recurrent genomic alterations between and within patients.

  Study EGAS00001003342

• Revealing active mutational processes in tumours using DigiPico/MutLX at unprecedented accuracy

  Active mutational processes in a tumor result in genetic micro-heterogeneity that can determine the tumor’s evolutionary trajectory. Identification of these processes by studying micro-heterogeneity can unveil novel aspects of tumor evolution with potential therapeutic implications. Such studies, however, are inherently problematic because of the discovery of excessive false positive mutations. Here we report the optimization and validation of a robust whole genome sequencing and analysis pipeline (DigiPico/MutLX) that virtually eliminates false positive results. Using our method, we identified, for the first time, a sub-clonal local hyper-mutation (kataegis) event in a recurrent ovarian carcinoma, which was unidentifiable from the bulk WGS data of the same tumor. Overall, we propose DigiPico/MutLX method as a powerful framework for the reliable study of genetic micro-heterogeneity in tumor and normal tissues.

  Study EGAS00001003555

• Whole genome sequencing with linked reads of pediatric glioblastoma samples

  Pediatric glioblastoma (pGBM) is a lethal cancer with no effective therapies. To understand mechanisms of tumor evolution in this cancer, we performed whole genome sequencing with linked reads on longitudinally resected pGBM samples. Our analyses showed that all diagnostic and recurrent samples were collections of genetically diverse subclones. Clonal composition rapidly evolved at recurrence, with less than 8% of non-synonymous single nucleotide variants being shared in diagnostic-recurrent pairs. In order to track the origins of the mutational events we observed in pGBM, we generated whole genome datasets for two patients and their parents. These trios showed that genetic variants could be (i) somatic, (ii) inherited from a healthy parent, or (iii) arose de novo in the germlines of pGBM patients. Analysis of variant allele frequencies supported a model of tumor growth involving slow-cycling cancer stem cells that give rise to fast-proliferating progenitor-like cells and to non-dividing cells. Interestingly, radiation and anti-mitotic chemotherapeutics did not increase overall tumor burden upon recurrence. These findings support an important role for slow-cycling stem cell populations in contributing to recurrences, since slow-cycling cell populations are expected to be less prone to genotoxic stress induced by these treatments and to accumulate few mutations. Our results highlight the need for new targeted treatments that account for the complex functional hierarchies and genomic heterogeneity of pGBM.

  Study EGAS00001003432

• Genomic and immune profiling of pre-invasive lung adenocarcinoma

  Adenocarcinoma in situ and minimally invasive adenocarcinoma are the pre-invasive forms of lung adenocarcinoma. The genomic and immune profiles of these lesions are poorly understood. Here we report exome and transcriptome sequencing of 98 lung adenocarcinoma precursor lesions and 99 invasive adenocarcinomas. We have identified EGFR, RBM10, BRAF, ERBB2, TP53, KRAS, MAP2K1 and MET as significantly mutated genes in the pre/minimally invasive group. Classes of genome alterations that increase in frequency during the progression to malignancy are revealed. These include mutations in TP53, arm-level copy number alterations, and HLA loss of heterozygosity. Immune infiltration is correlated with copy number alterations of chromosome arm 6p, suggesting a link between arm-level events and the tumor immune environment.

  Study EGAS00001004006

• A Single-Cell Atlas of the Multicellular Ecosystem of Primary and Metastatic Hepatocellular Carcinoma

  Hepatocellular carcinoma (HCC) represents a paradigm of the relation between tumor microenvironment (TME) and tumor development. Here, we generated a single-cell atlas of the multicellular ecosystem of HCCs from four relevant sites. Antitumor central memory T (TCM) cells were found in tertiary lymphoid structures (TLSs). Chronic HBV/HCV infection increases infiltration of CD8+ T cells in tumors but aggravates T cell exhaustion. We identified CD11b+ macrophages to be terminally differentiated tumor-associated macrophages (TAMs) and two distinct differentiation trajectories contribute to their accumulation. We further demonstrated CD11b+ TAMs promote HCC cells invasion and migration, and angiogenesis. We characterized the heterogeneous subpopulations of malignant hepatocytes and their multifaceted roles in shaping the immune microenvironment. Finally, we identified seven TME subtypes that can predict patient prognosis. Collectively, this large-scale atlas deepens our understanding of the ecosystem in primary and metastatic HCCs, might facilitating the development of new immune therapy strategies for this malignancy.

  Study EGAS00001004468

• glioblastoma and glioblastoma stem cells (GSCs), RNA-seq and WGS

  Characterization of glioblastoma tumors and glioblastoma stem cells (GSCs) lines via RNA-seq and/or WGS profiling.

  Study EGAS00001004395

• Identification and functional characterisation of a rare MTTP variant underlying familial non-alcoholic fatty liver disease

  We identified a rare causal variant in MTTP, c.1691T>C p.I564T (rs745447480) encoding microsomal triglyceride transfer protein (MTP) causing progressive non-alcoholic fatty liver disease with cirrhosis and hepatocellular carcinoma unrelated to metabolic syndrome, without manifestations of abetalipoproteinemia, in a four generation family with South Asian ancestry. Variant-expressing hepatocyte-like-cells (HLCs) derived from human induced pluripotent stem cells generated from homozygous donor skin fibroblasts had lower lipoprotein ApoB secretion, compared to wild type cells. Cytoplasmic triglyceride accumulation in HLCs triggered endoplasmic reticulum stress, secretion of pro-inflammatory mediators, production of reactive oxygen species, delineating the progression of disease associated with homozygosity for MTTP p.I564T

  Study EGAS00001005254

• Evolutionary predictability of genetic versus non genetic resistance to anticancer drugs in melanoma

  Therapy resistance arises from heterogeneous drug-tolerant persister cells or minimal residual disease (MRD) through genetic and nongenetic mechanisms. A key question is whether specific molecular features of the MRD ecosystem determine which of these two distinct trajectories will eventually prevail. We show that, in melanoma exposed to MAPK therapeutics, emergence of a transient neural crest stem cell (NCSC) population in MRD concurs with the development of nongenetic resistance. This increase relies on a GDNF-dependent signaling cascade, which activates the AKT survival pathway in a focal adhesion kinase (FAK)-dependent manner. Ablation of the NCSC population through FAK inhibition delays relapse in patient-derived tumor xenografts. Strikingly, all tumors that ultimately escape this treatment exhibit resistance-conferring genetic alterations and increased sensitivity to ERK inhibition. These findings identify an approach that abrogates the nongenetic resistance trajectory in melanoma and demonstrate that the cellular composition of MRD deterministically imposes distinct drug resistance evolutionary paths.

  Study EGAS00001005314

• UQCCR/QCMG brain metastasis sequence analysis

  The present study, conducted by the UQCCR and QCMG at the University of Queensland, investigated the genomic landscape of brain metastases from 36 patients (with matched normal samples) that originated from a range of primary tumour sites (including breast (11), lung (18), melanoma (6) and oesophageal (1)) utilizing Illumina 2500 HiSeq Exome sequencing. Somatic substitutions were identified using a dual qSNP/GATK calling strategy. The Pindel tool was used to determined short insertions and deletions. Key biological pathways were investigated using pathway analysis tools. It is envisaged that the current work may provide important biological insights into the genomic characterization and evolution of brain metastasis and additionally identify druggable targets of clinical relevance.

  Study EGAS00001000722

• A renal cell carcinoma tumorgraft platform to advance precision medicine

  Study EGAS00001005516

• ICGC PedBrain: Deep-sequencing of childhood brain tumors.

  Brain tumors constitute the childhood tumors with the highest mortality rates and survival is often associated with severe long-term sequelae due to intensive multimodal treatment regimens. Thus, the PedBrainTumor network aims at the identification of robust biomarkers for clinical management and novel molecular targets for therapeutic interventions. Using massively parallel whole-genome sequencing, and transcriptome and methylome sequencing for subsets of cases, we aim to decipher the molecular causes of medulloblastoma as well as low-grade astrocytoma, and to identify markers to stratify patients for specialized treatment. As the PedBrain network is affiliated with clinical trials in medulloblastoma and pilocytic astrocytoma, validation cohorts for the translation of key findings into clinical settings are readily available.

  Study EGAS00001000215

• Fibroblast heterogeneity and immunosuppressive environment in Human breast cancer

  Carcinoma-associated fibroblasts (CAF) are key players in the tumor microenvironment. Here, by combining 6 well-known stromal markers, we identify four CAF subsets (CAF-S1 to CAF-S4) in human breast cancer (BC) that exhibit distinct activation patterns and accumulate differently in BC subtypes.

  Study EGAS00001002508

• Paired exome analysis in urothelial carcinoma

  In this study we characterized genomic alterations in two to five metachronous tumors from 29 patients initially diagnosed with early stage disease. Fourteen patients (32 tumors) had non progressive disease (NPD) and 15 patients (34 tumors) had progressive disease (PD). Whole exome sequencing (WES, ~50x mean read depth), Ultra deep targeted sequencing (~6,809x mean read depth) and whole transcriptome RNA-seq was performed for all samples. In addition multiregional WES was performed on 8 adjacent regions from a single tumor.

  Study EGAS00001001686

• Spatio-temporal evolution of the primary glioblastoma genome (newly added after 2015)

  Since our genomic analysis of 38 GBM patients, we recruited more cases to further study spatial and temporal evolution of glioblastoma with international collaboration of multiple institutes. The cases in this study includes the whole exome and transcriptome of GBM patients newly recruited in Samsung Medical Center.

  Study EGAS00001001800

• WGS of T-cell and NK-cell lymphoma for ICGC (NKTL-SG)

  Lymphomas are malignancies originating from the lymphatic system and there is a worldwide significant increase in incidences of this cancer. Lymphomas can be further classified into 2 categories namely Hodgkin’s (HL), and Non-Hodgkin’s lymphoma (NHL). The latter can be divided further into B, T and NK cell NHL. Peripheral T-cell lymphoma (PTCL) and Natural Killer/ T-cell lymphoma (NKTL) arise from mature T lymphocytes and NK cells. The more common subtypes include NKTL, PTCL not-otherwise-specified (PTCL-NOS), systemic anaplastic large cell lymphoma (ALCL), follicular PTCL (F-PTCL), angioimmunoblastic T-cell lymphoma (AITL) and enteropathy-associated T-cell lymphoma (EATL). Whole genome analyses on T-cell and NK-cell lymphoma samples from various countries to gain a better understanding of the pathogenesis and biology of these tumors. The comprehensive profiling and comparison among patients from various countries with different etiopathogenesis will hopefully facilitate the elucidation of key pathways and underlying molecular mechanisms driving these cancers, opening up novel therapeutic strategies in the future.

  Study EGAS00001002398

• A combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancer

  The earlier diagnosis of cancer is one of the keys to reducing future cancer deaths. Here we describe our efforts to develop a non-invasive, blood test for the detection of pancreatic ductal adenocarcinoma. We combined blood tests for KRAS gene mutations with carefully thresholded protein biomarkers to determine whether the combination of these markers was superior to any single marker. The cohort tested included 221 patients with resectable pancreatic ductal adenocarcinoma and 185 control patients without known cancers. KRAS mutations were detected in the plasma of 66 patients (30%) and every mutation found in the plasma was identical to that subsequently found in the patients' primary tumor (100% concordance). The use of KRAS in conjunction with two thresholded protein biomarkers (CA19-9 and prolactin) increased the sensitivity to 67%. Only one of the 185 plasma samples from the control cohort were positive for any of the three markers (99.5% specificity). This combinatorial approach may prove useful for the earlier detection of many cancer types.

  Study EGAS00001002444

• Genome and transcriptome sequence data from a infantile fibrosarcoma tumor patient

  Genome and transcriptome sequence data from a infantile fibrosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015263

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015264

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015265

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015267

• Genome and transcriptome sequence data from a CNS sarcoma tumor patient

  Genome and transcriptome sequence data from a CNS sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015268

• Genome and transcriptome sequence data from a ocular melanoma tumor patient

  Genome and transcriptome sequence data from a ocular Melanoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015269

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015270

• Genome and transcriptome sequence data from a angiosarcoma tumor patient

  Genome and transcriptome sequence data from a angiosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015272

• Genome and transcriptome sequence data from a craniopharyngioma tumor patient

  Genome and transcriptome sequence data from a craniopharyngioma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015273

• Genome and transcriptome sequence data from a papillary thyroid carcinoma tumor patient

  Genome and transcriptome sequence data from a papillary thyroid carcinoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015276

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015277

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015278

• Genome and transcriptome sequence data from a pineoblastoma tumor patient

  Genome and transcriptome sequence data from a pineoblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015280

• Genome and transcriptome sequence data from a multifocal glioblastoma multiforme tumor patient

  Genome and transcriptome sequence data from a multifocal glioblastoma multiforme patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015281

• Genome and transcriptome sequence data from a progressive facial plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a progressive facial plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015282

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015283

• Genome and transcriptome sequence data from a diffuse intrinsic pontine glioma tumor patient

  Genome and transcriptome sequence data from a diffuse Intrinsic Pontine Glioma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015284

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015286

• Genome and transcriptome sequence data from a ependymoma tumor patient

  Genome and transcriptome sequence data from a ependymoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015287

• Genome and transcriptome sequence data from a glioblastoma tumor patient

  Genome and transcriptome sequence data from a glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015288

• Genome and transcriptome sequence data from a NUT midline carcinoma tumor patient

  Genome and transcriptome sequence data from a NUT midline carcinoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015289

• Genome and transcriptome sequence data from a angiosarcoma tumor patient

  Genome and transcriptome sequence data from a angiosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015290

• Genome and transcriptome sequence data from a gliomatosis cerebri anaplastic astrocytoma tumor patient

  Genome and transcriptome sequence data from a gliomatosis cerebri anaplastic astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015292

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015293

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015294

• Genome and transcriptome sequence data from a metastatic alveolar rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a metastatic alveolar rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015295

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015296

• Genome and transcriptome sequence data from a minimally invasive adenocarcinoma tumor patient

  Genome and transcriptome sequence data from a minimally invasive adenocarcinoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015297

• Genome and transcriptome sequence data from a glioblastoma tumor patient

  Genome and transcriptome sequence data from a glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015300

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015302

• Genome and transcriptome sequence data from a papillary thyroid carcinoma tumor patient

  Genome and transcriptome sequence data from a papillary thyroid carcinoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015303

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015305

• Genome and transcriptome sequence data from a metastatic osteosarcoma tumor patient

  Genome and transcriptome sequence data from a metastatic osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015306

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma of the nasopharynx tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma of the nasopharynx patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015307

• Genome and transcriptome sequence data from a diffuse aarge B-cell lymphoma (relapse) tumor patient

  Genome and transcriptome sequence data from a diffuse large B-cell lymphoma (relapse) patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015308

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015309

• Genome and transcriptome sequence data from a relapsed osteosarcoma tumor patient

  Genome and transcriptome sequence data from a relapsed osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015311

• Genome and transcriptome sequence data from a synovial sarcoma tumor patient

  Genome and transcriptome sequence data from a synovial sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015313

• Genome and transcriptome sequence data from a recurrence nasopharyngeal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a recurrence nasopharyngeal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015314

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015315

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015316

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015318

• Genome and transcriptome sequence data from a anaplastic astrocytoma tumor patient

  Genome and transcriptome sequence data from a anaplastic astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015319

• Genome and transcriptome sequence data from a choroid plexus carcinoma tumor patient

  Genome and transcriptome sequence data from a choroid plexus carcinoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015321

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015324

• Genome and transcriptome sequence data from a NUT midline carcinoma tumor patient

  Genome and transcriptome sequence data from a NUT midline carcinoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015325

• Genome and transcriptome sequence data from a diffuse midline glioma, H3K27 mutant tumor patient

  Genome and transcriptome sequence data from a diffuse midline glioma, H3K27 mutant patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015326

• Genome and transcriptome sequence data from a CNS non-germinoma germ cell tumour tumor patient

  Genome and transcriptome sequence data from a CNS non-germinoma germ cell tumour patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015327

• Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015329

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015330

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015332

• Genome and transcriptome sequence data from a pIlomyxoid astrocytoma tumor patient

  Genome and transcriptome sequence data from a pIlomyxoid astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015333

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015335

• Genome and transcriptome sequence data from a high-grade glioma, glioblastoma tumor patient

  Genome and transcriptome sequence data from a high-grade glioma, glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015336

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015337

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015338

• Multiome_HB

  Hepatoblastoma (HB) cells display strong phenotypic heterogeneity with a major impact on drug response, but the underlying mechanisms are poorly understood. Here, we use a single-cell multi-omic strategy to unravel the molecular determinants of this plasticity. HB display a continuum of single-cell states between hepatocytic (scH), liver progenitor (scLP) and mesenchymal (scM) differentiation poles, with an intermediate scH/LP population bordering scLP and scH areas in spatial transcriptomics. Chromatin accessibility landscapes reveal the gene regulatory networks of each differentiation pole, and the sequence of transcription factor activation underlying cell state transitions. Single-cell mapping of somatic alterations reveals the clonal architecture of each tumor, showing that each genetic subclone displays its own range of cellular plasticity across differentiation poles. The most scLP subclones, overexpressing stem cell and DNA repair genes, proliferate faster after neo-adjuvant chemotherapy. These results highlight how the interplay of clonal evolution and epigenetic plasticity shapes the potential of HB subclones to respond to chemotherapy.

  Study EGAS00001006932

• Exome and transcriptome sequencing of Desmoplastic Small Round Cell Sarcoma

  To conduct exome and transcriptome profiling to identify secondary recurrent mutations, multi-site phylogenetic analyse and immune characterization of Desmoplastic Small Round Cell Sarcoma.

  Study EGAS00001004575

• Genomic and epigenomic insights into the origin, pathogenesis and clinical behavior of mantle cell lymphoma subtypes

  Mantle Cell Lymphoma can be subdivided into conventional (cMCL) and leukemic non-nodal MCL (nnMCL), which differ in the cell of origin, gene expression, biological characteristics and clinical evolution. Using genomic, epigenomic and transcriptomic data we identified common and particular alterations in the two subtypes, either point mutations, copy number alterations or structural variants some of them with prognostic impact.

  Study EGAS00001004165

• Genomic landscape and PD-1 blockade in Natural-killer/T cell lymphoma

  Natural killer/T-cell lymphoma (NKTL) is an aggressive malignancy with a predilection for Asian, Mexican and South American populations. With the exception of Japan, it is the most common mature T-cell lymphoma in Asia. NKTL presents as extranodal disease and mostly affects the upper aerodigestive tract. Neoplastic cells are invariably infected by the Epstein-Barr virus (EBV) and characterized by a cytotoxic phenotype. The genetic landscape of NKTL has been recently unraveled by discoveries describing recurring mutations altering the JAK-STAT pathway, epigenetic modifiers, the DDX3X gene and genetic predisposition in the HLA-DPB1 gene.The genomic landscape of NKTL has been interrogated by whole-exome sequencing, targeted sequencing and single nucleotide polymorphism arrays, but has yet been studied with whole-genome sequencing (WGS). In this study, we will use a combination of WGS, targeted-capture sequencing (TCS) and RNA sequencing data to explore the genetic landscape and its relevance to immunotherapy in NKTL

  Study EGAS00001003828

• Tumorigenic role of Musashi-2 in aggressive mantle cell lymphoma

  SOX11 is overexpressed in the highly aggressive conventional mantle cell lymphoma (MCL) but rarely in the indolent non-nodal MCL subtype. We performed RNA-seq of SOX11+ and SOX11- MCLs to search for SOX11-regulated genes that contributed to MCL clinical evolution, identifying Musashi-2 (MSI2) stem cell-related gene. By performing RNA-seq upon MSI2 knockdown or inhibition in MCL, we identified MSI2 as a potential target to inhibit drug resistance in aggressive MCLs.

  Study EGAS00001006613

• Transcriptome sequencing of hepatocellular carcinoma biopsies (proteogenomic study)

  We performed a proteogenomic analysis of hepatocellular carcinomas (HCCs) across clinical stages and etiologies to define the deregulated pathways and heterogeneity among HCCs. We identified pathways that are differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. Integrative clustering identified HCC subgroups that show distinct regulation of biological processes, metabolic reprogramming and kinase activation.

  Study EGAS00001005074

• Comprehensive Transcriptional Analysis of Early Stage Urothelial Carcinoma using whole transcriptome sequencing

  The expression profile and sequence variants of 476 early stage urothelial carcinoma were studied using whole transcriptome sequencing. RNA-Seq libraries were prepared by Ribo-Zero treatment of total-RNA followed by library preparation using ScriptSeq (both Epicentre/Illumina). RNA-Seq libraries were paired-end sequenced (2x 101 bp) on Illumina HiSeq 2000 and the resulting fastq files were processed using tools from the Genome Analysis Toolkit (GATK and from the Tuxedo suite. Access to the sequence data (mapped and un-mapped bam and vcf files), containing person identifying information, needs signature on a controlled access form, and can be accessed at The European Genome-phenome Archive (EGA) following request. An expression matrice of FPKM values are available without restriction at ArrayExpress (E-MTAB-4321).

  Study EGAS00001001236

• Genomic and transcriptomic landscape of T-cell lymphoblastic lymphoma compared to T-cell acute lymphoblastic leukemia: similar subtypes and different fusions

  Due to the lower incidence of T-LBL and difficulties in obtaining diagnostic T-LBL material, extensive research on T-LBL has been hampered whereas genetic aberrations in T-ALL are thoroughly characterized. Given the similarities and differences between T-LBL and T-ALL, the question has been raised whether T-LBL and T-ALL represent two different diseases or different manifestations of the same disease. This study aims to identify the genomic and transcriptomic landscape of T-LBL and compare the findings to what is found T-ALL. Comparison of the molecular aberrations between T-LBL and T-ALL can provide insights into the overlap and differences in malignant development between the two entities, which could lead to improved risk stratification in T-LBL in order to eventually adapt T-LBL treatment protocols based on molecular-genetic prognostic factors.

  Study EGAS00001007767

• Machine Learning Guided Signal Enrichment for Plasma Tumor-burden Monitoring Dataset

  Includes 4 Datasets: 1. 35 Control Plasma WGS samples sequenced on NovaSeq V1.0 Chemistry at the New York Genome Center. Denoted by CTRL-2XX naming scheme. 2. Plasma WGS from 17 patients with Small Cell Lung Cancer. Samples extracted at either Pretreatment or Postoperative at weeks 2 or 3. Denoted by SCLC-XX naming scheme. 3. Plasma WGS from a synthetic mixing study of a high-burden melanoma plasma sample with plasma bag, at estimated tumor concentrations of 10e-3, 10e-4, 10e-5, and 10e-6 for 2 replicates. Denoted by SM-repX naming scheme. 4. Assorted Plasma, Tumor, and Normal WGS from patients with NSCLC expressing high-burden for use in model training. Denoted by NSCLC-2XX naming scheme.

  Dataset EGAD00001011352

• Genomic and transcriptomic landscape of T-cell lymphoblastic lymphoma compared to T-cell acute lymphoblastic leukemia: similar subtypes and different fusions

  Due to the lower incidence of T-LBL and difficulties in obtaining diagnostic T-LBL material, extensive research on T-LBL has been hampered whereas genetic aberrations in T-ALL are thoroughly characterized. Given the similarities and differences between T-LBL and T-ALL, the question has been raised whether T-LBL and T-ALL represent two different diseases or different manifestations of the same disease. This study aims to identify the genomic and transcriptomic landscape of T-LBL and compare the findings to what is found T-ALL. Comparison of the molecular aberrations between T-LBL and T-ALL can provide insights into the overlap and differences in malignant development between the two entities, which could lead to improved risk stratification in T-LBL in order to eventually adapt T-LBL treatment protocols based on molecular-genetic prognostic factors.

  Dataset EGAD00001015357

• FASTQ file for paper titled "Discovery of immunotherapy targets for pediatric solid and brain tumors by exon-level expression"

  This data set includes 27 full-length transcript sequence generated from PacBio IsoSeq that were used for verify the cancer-specific exons identified in three genes: FN1, COL6A3 and TNC. The data were generated from PDX models of osteosarcoma patients.

  Dataset EGAD00001015356

• Chromatin run-on and RNA sequencing of fibrolamellar carcinoma

  Fibrolamellar carcinoma (FLC) is a rare, therapeutically intractable liver cancer that disproportionately affects youth. Here we used chromatin run-on sequencing to characterize the enhancer landscape in FLC and RNA sequencing to identify dysregulated genes in FLC.

  Study EGAS00001004169

• LOGGIC Core BioClinical Data Bank: Added clinical value of RNA-Seq in an international molecular diagnostic registry for pediatric low-grade glioma patients

  Study EGAS00001007072

• Overexpression of the miR-17-92 cluster in colorectal adenoma organoids induces a carcinoma-like genotype

  Study EGAS00001005949

• Cancerous Adaptive Dosing Melanoma WGS Dataset

  Illumina NovaSeq 6000 30x WGS of 26 samples, each with up to 5 matched timepoints. Timepoints A,B,C,D,and E correspond to Pretreatment, Week 3, Week 6, Week 9, and Week 12 after treatment, respectively. Additional sample metadata (sample recurrence, treatment course, age, sex, comorbidities, etc.) are present in sample description.

  Dataset EGAD00001010926

• Healthy_pleura

  EPIC methylation data of pleural mesothelioma samples and healthy pleura samples

  Dataset EGAD00010002656

• Pleural_mesothelioma

  EPIC methylation data of pleural mesothelioma samples and healthy pleura samples

  Dataset EGAD00010002657

• Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma

  We examined pre-treatment tumors from participants of the IMmotion150 trial. Biomarker analyses indicate that high angiogenesis gene expression was associated with improved PFS within the sunitinib arm, and high T-effector/IFNγ response (Teff) gene expression with longer PFS in atezolizumab + bevacizumab vs sunitinib patients. In high Teff tumors, concomitant high myeloid inflammation was associated with worse PFS in the atezolizumab monotherapy arm; adding bevacizumab to atezolizumab in this subgroup improved PFS vs atezolizumab. Molecular profiles suggest that prediction of outcomes with VEGF inhibitors and immunotherapy may be possible in mRCC, and bevacizumab may overcome myeloid inflammation-associated atezolizumab resistance.

  Study EGAS00001002928

• Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell RCC-- CA209-025

  Immune checkpoint inhibitors targeting the PD-1 pathway have transformed the management of many advanced malignancies, including clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of PD-1 response remain incompletely elucidated. Here, we analyzed 592 tumors collected from advanced ccRCC patients enrolled in prospective clinical trials of treatment with PD-1 blockade (or mTOR inhibition as control arm) by whole-exome and RNA-sequencing, integrated with immunofluorescence analysis, to define the somatic alteration landscape of late-stage ccRCC and to uncover the immunogenomic determinants of therapeutic response. While conventional genomic markers (tumor mutation burden, neoantigen load) and degree of CD8+ T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations in advanced ccRCC associated with response or resistance to PD-1 blockade. These advanced tumors were highly CD8+ T cell infiltrated, with only 22% and 5% with an immune desert and immune excluded phenotype, respectively. Our analysis revealed that CD8+ infiltrated tumors are depleted of favorable PBRM1 mutations and are enriched for unfavorable chromosomal losses of 9p21.3 when compared to non-infiltrated tumors. These data demonstrate how the interplay of somatic alterations and immunophenotypes impacts therapeutic efficacy.

  Study EGAS00001004292

• Activation of Wnt/β-catenin signalling by mutually exclusive FBXW11 and CTNNB1 hotspot mutations drives salivary gland basal cell adenoma - WES

  Wnt signalling must be 'just right' to promote tumour growth. Basal cell adenoma (BCA) and basal cell adenocarcinoma (BCAC) of the salivary gland are rare tumours that can be difficult to distinguish from each other and other salivary gland tumour subtypes. Due to their rarity, the genomic profiles of BCA and BCAC have not been explored. Using whole-exome and transcriptome sequencing of BCA and BCAC cohorts, we identify a novel recurrent FBXW11 missense mutation (p.F517S) in BCA, that was mutually exclusive with the previously reported CTNNB1 p.I35T gain-of-functon (GoF) mutation. These driver events collectively accounted for 94% of BCAs. In vitro, mutant FBXW11 had a dominant negative affect, characterised by defective binding to β-catenin and the accumulation of β-catenin in cells. This was consistent with the nuclear expression of β-catenin observed in BCA cases harbouring the FBXW11 p.F517S mutation and activation of the Wnt/β-catenin pathway and defines a novel mechanism of Wnt pathway control. The genomic profiles of BCAC were distinct from BCA, with hotspot DICER1 and HRAS mutations and putative driver mutations affecting PI3K/AKT and NF-κB signalling pathway genes. A single BCAC, which may represent a malignant transformation of BCA, harboured the recurrent FBXW11 mutation. These findings have important implications for the diagnosis and treatment of BCA and BCAC, which, despite histopathologic overlap, may be unrelated entities.

  Dataset EGAD00001015365

• Activation of Wnt/β-catenin signalling by mutually exclusive FBXW11 and CTNNB1 hotspot mutations drives salivary gland basal cell adenoma - RNA

  Wnt signalling must be 'just right' to promote tumour growth. Basal cell adenoma (BCA) and basal cell adenocarcinoma (BCAC) of the salivary gland are rare tumours that can be difficult to distinguish from each other and other salivary gland tumour subtypes. Due to their rarity, the genomic profiles of BCA and BCAC have not been explored. Using whole-exome and transcriptome sequencing of BCA and BCAC cohorts, we identify a novel recurrent FBXW11 missense mutation (p.F517S) in BCA, that was mutually exclusive with the previously reported CTNNB1 p.I35T gain-of-functon (GoF) mutation. These driver events collectively accounted for 94% of BCAs. In vitro, mutant FBXW11 had a dominant negative affect, characterised by defective binding to β-catenin and the accumulation of β-catenin in cells. This was consistent with the nuclear expression of β-catenin observed in BCA cases harbouring the FBXW11 p.F517S mutation and activation of the Wnt/β-catenin pathway and defines a novel mechanism of Wnt pathway control. The genomic profiles of BCAC were distinct from BCA, with hotspot DICER1 and HRAS mutations and putative driver mutations affecting PI3K/AKT and NF-κB signalling pathway genes. A single BCAC, which may represent a malignant transformation of BCA, harboured the recurrent FBXW11 mutation. These findings have important implications for the diagnosis and treatment of BCA and BCAC, which, despite histopathologic overlap, may be unrelated entities.

  Dataset EGAD00001015366

• Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma

  Study EGAS00001004288