5032 results for "*oma"

in 67.47 milliseconds.

• 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

• CcRCC_metabolic_heterogeneity

  Heterogeneity is a hallmark of clear cell renal cell carcinoma (ccRCC). In this study, hyperpolarized [1-13C]pyruvate MRI (HP-13C-MRI) was used to probe metabolic heterogeneity non-invasively in 6 ccRCC patients from which 58 tumor and healthy tissues biopsies were acquired postoperatively. MRI parameters were correlated with 146 metabolite concentrations and the expression of 2523 metabolic genes across 34 metabolic pathways. Using metabologram projections and pathway consensus scoring, several metabolic pathways were correlated with the metabolic MRI, including glycolysis, pentose phosphate pathway, and the TCA cycle. A simple thresholding approach applied to the imaging metrics was sufficient to provide meaningful metabolic pathway information. We also show that tissue metabolic heterogeneity increases in regions with higher pyruvate-to-lactate conversion on imaging. These results provide validation of the biological relevance of HP-13C-MRI and support the possibility of its clinical use to improve disease characterization, stratification, targeting of biopsies, and providing novel methods to monitor treatment response.

  Dataset EGAD00001015780

• A developmental cell atlas of the human thyroid gland

  Thyroid hormones are essential for health, but human thyroid development and cellular heterogeneity are poorly understood. We generated a high-resolution, spatiotemporal transcriptional atlas of human fetal thyroid, and examined its perturbations in trisomy 21 (T21), where aberrant thyroid development is associated with congenital hypothyroidism, and in papillary thyroid carcinoma (PTC). Normal tissue revealed two functional follicular cell states (fTFC1, fTFC2) with divergent PAX8 expression levels, persisting into adulthood. Additionally, the rare C-cell population was comprehensively profiled at single-cell resolution for the first time. T21 fetal thyroid was hypoplastic, with disrupted follicular morphology and altered expression of genes mediating cellular topology and extracellular matrix interactions. Finally, the transcriptional signals of both fetal follicular cell states were found to be perturbed in PTC. Overall, we defined a reference atlas for normal human fetal thyrocyte heterogeneity and provided novel insights into thyroid disorders.

  Dataset EGAD00001015783

• Dataset for H3K27me3 ChIP-seq data for neuroblastoma cell lines (Neuroblastoma_CL)

  Dataset for H3K27me3 ChIP-seq data of 31 neuroblastoma tumor samples. The sequencing was performed on Illumina HiSeq 2000 and Illumina HiSeq 2500

  Dataset EGAD00001015809

• Dataset for Transcriptomic sequencing data for neuroblastoma tumor samples

  Dataset Transcriptomic sequencing data of 42 neuroblastoma tumor samples. The sequencing was performed on Illumina HiSeq 2000 and Illumina HiSeq 4000. The sequencing was always paired.

  Dataset EGAD00001015812

• Dataset for ChIP-seq data for neuroblastoma tumor samples(NB,Neuroblastoma)

  Dataset for H3K27me3 ChIP-seq data of neuroblastoma tumor samples. The sequencing was performed on Illumina HiSeq 2000 and Illumina HiSeq 2500

  Dataset EGAD00001015813

• 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 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 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 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 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 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 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 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 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

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

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

  Dataset EGAD00001005908

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

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

  Dataset EGAD00001005849

• Genome and transcriptome sequence data from a tongue squamous cell carcinoma (h&n) patient

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

  Dataset EGAD00001005865

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

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

  Dataset EGAD00001005874

• Genome and transcriptome sequence data from a lung adenocarcinoma patient

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

  Dataset EGAD00001005880

• Genome and transcriptome sequence data from a parotid gland adenoid cystic carcinoma patient

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

  Dataset EGAD00001005887

• 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 EGAD00001005890

• Genome and transcriptome sequence data from a ewing sarcoma patient

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

  Dataset EGAD00001005894

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

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

  Dataset EGAD00001005903

• Genome and transcriptome sequence data from a lung adenocarcinoma patient

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

  Dataset EGAD00001008926

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

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

  Dataset EGAD00001008915

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

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

  Dataset EGAD00001008912

• Genome and transcriptome sequence data from a nasopharynx carcinoma patient

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

  Dataset EGAD00001008917

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

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

  Dataset EGAD00001008920

• Genome and transcriptome sequence data from a salivary carcinoma patient

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

  Dataset EGAD00001008937

• Genome and transcriptome sequence data from a neuroendocrine carcinoma patient

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

  Dataset EGAD00001008946

• Genome and transcriptome sequence data from a metastatic endometrioid/mucinous ovarian carcinoma patient

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

  Dataset EGAD00001004636

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

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

  Dataset EGAD00001004642

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

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

  Dataset EGAD00001004654

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

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

  Dataset EGAD00001004657

• Genome and transcriptome sequence data from a neuroendocrine carcinoma patient

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

  Dataset EGAD00001004664

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

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

  Dataset EGAD00001004700

• Genome and transcriptome sequence data from a metastatic adenoid cystic carcinoma of the palate patient

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

  Dataset EGAD00001003065

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

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

  Dataset EGAD00001003067

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

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

  Dataset EGAD00001003073

• Genome and transcriptome sequence data from a metastatic paraganglioma patient

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

  Dataset EGAD00001003636

• Genome and transcriptome sequence data from a salivary duct carcinoma patient

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

  Dataset EGAD00001003627

• Genome and transcriptome sequence data from a metastatic melanoma patient

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

  Dataset EGAD00001003603

• Genome and transcriptome sequence data from a metastatic small cell carcinoma patient

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

  Dataset EGAD00001003608

• Genome and transcriptome sequence data from a metastatic non-small cell lung carcinoma patient

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

  Dataset EGAD00001003614

• 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 EGAD00001003642

• Genome and transcriptome sequence data from a metastatic adenoid cystic carcinoma patient

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

  Dataset EGAD00001003640

• 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 EGAD00001003657

• Genome and transcriptome sequence data from a metastatic adenoid cystic carcinoma patient

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

  Dataset EGAD00001003679

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

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

  Dataset EGAD00001003691

• Genome and transcriptome sequence data from a pleomorphic sarcomatoid epithelioid carcinoma patient

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

  Dataset EGAD00001003694

• Genome and transcriptome sequence data from a t-cell rich b cell lymphoma patient

  Genome and transcriptome sequence data from a t-cell rich b cell lymphoma patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003724

• Genome and transcriptome sequence data from a sinus adenocarcinoma patient

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

  Dataset EGAD00001003737

• Genome and transcriptome sequence data from a pleomorphic xanthoastrocytoma patient

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

  Dataset EGAD00001003744

• Genome and transcriptome sequence data from a non-small cell adenocarcinoma patient

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

  Dataset EGAD00001004600

• Genome and transcriptome sequence data from a non-small cell lung carcinoma patient

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

  Dataset EGAD00001004626

• Genome and transcriptome sequence data from a metastatic follicular thyroid carcinoma patient

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

  Dataset EGAD00001004614

• Genome and transcriptome sequence data from a metastatic melanoma patient

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

  Dataset EGAD00001004610

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

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

  Dataset EGAD00001004668

• Genome and transcriptome sequence data from a metastatic high-grade adenocarcinoma of the fallopian tubes patient

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

  Dataset EGAD00001004709

• 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 EGAD00001010943

• 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 EGAD00001010946

• Genome and transcriptome sequence data from a merkel cell carcinoma patient

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

  Dataset EGAD00001010955

• Genome and transcriptome sequence data from a metastatic melanoma patient

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

  Dataset EGAD00001010958

• Genome and transcriptome sequence data from a metastatic ​adenocarcinoma of breast (ductal) patient

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

  Dataset EGAD00001010976

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

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

  Dataset EGAD00001010977

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

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

  Dataset EGAD00001010979

• Genome and transcriptome sequence data from a lung adenocarcinoma patient

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

  Dataset EGAD00001010987

• Genome and transcriptome sequence data from a unknown – likely pancreatobiliary / intrahepatic cholangiocarcinoma in liver patient

  Genome and transcriptome sequence data from a unknown – likely pancreatobiliary / intrahepatic cholangiocarcinoma in liver patient,generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001010989

• Genome and transcriptome sequence data from a metastatic lymphoepithelial carcinoma of the parotid gland patient

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

  Dataset EGAD00001010993

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

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

  Dataset EGAD00001011011

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

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

  Dataset EGAD00001011014

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

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

  Dataset EGAD00001011017

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

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

  Dataset EGAD00001011021

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

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

  Dataset EGAD00001011026

• Genome and transcriptome sequence data from a nasopharyngeal carcinoma patient

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

  Dataset EGAD00001011028

• Genome and transcriptome sequence data from a colorectal carcinoma patient

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

  Dataset EGAD00001011029

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

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

  Dataset EGAD00001011035

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

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

  Dataset EGAD00001011038

• 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

• Molecular characterisation of paediatric PDX cells before and after 3D RASTRUM bioprinting

  This dataset is a part of the publication and contains source data for comparative analysis of the molecular (TSO500, RNAseq) characteristics between patient-derived xenograft (PDX) cells from high-risk paediatric neuroblastoma and sarcoma models before and after 3D bioprinting using RASTRUM Bioprinter.

  Study EGAS00001008220

• WGS files for paper titled "Sensitive detection of MYCN amplified neuroblastoma in blood samples with structural variants using whole genome sequencing"

  This dataset contains one long read whole genome sequence tumor sample. 2.5µg of patient DNA was used to prepare SMRTbell sequencing library following the “Preparing whole genome and metagenome libraries using SMRTbell® prep kit 3.0” protocol (PacBio). Sequencing was conducted on PacBio Revio+SPRQ chemistry with 250pM loading concentration, generating 35x mean coverage for the whole genome. Reads were mapped to human genome assembly GRCh37 using pbmm2 v1.17.0 (https://github.com/PacificBiosciences/pbmm2)

  Dataset EGAD00001016133

• The Spatial Heterogeneity in Multiple Myeloma - from the Subclonal Architecture to the Immune Microenvironment (partly hipo_K08K)

  Multiple myeloma (MM) cells show pronounced heterogeneity not only within a given patient but also between spatially separated tumor loci in the bone marrow. Understanding this spatial heterogeneity is emerging as a critical challenge for the successful treatment of the disease. Yet, our understanding of spatial differences in the subclonal architecture, molecular signatures and interactions with the tumor microenvironment remains very limited. To address this shortcoming, we performed bulk and single-cell multi-region sequencing, including random bone marrow samples from the iliac crest and paired imaging-guided focal lesion specimens from 15 newly diagnosed MM patients. We found a median of 5 subclones per patient and unique subclones in focal lesions. Central features of spatial heterogeneity included a consistent down-regulation of the chemoattractant cytokines CXCL7 and CXCL12 in focal lesion tumor cells as well as a significant depletion of macrophages in the focal lesion microenvironment. In contrast, a site-specific expansion of T-cell clones was not detected in focal lesions. In conclusion, our results demonstrate the relevance of considering spatial heterogeneity with potential implications for immunotherapies and molecular studies analyzing the role of gene signatures and MM-microenvironment interactions in disease progression.

  Study EGAS00001006090

• Epigenomic profiling of neuroendocrine lung cancer

  Heterogeneity of neuroendocrine (NE) differentiation in pulmonary high-grade NE carcinoma, mainly small-cell lung cancer (SCLC), has been recently explored based on the expression of lineage transcription factors such as ASCL1, NEUROD1 and POU2F3. However, molecular classification determined by these factors is not settled and accumulating evidence suggest that NE differentiation among lung cancers is more heterogeneous. Here, we investigated super-enhancer profiles of formalin-fixed paraffin-embedded tumor tissues from patients with a wide range of lung cancer with NE components, mainly large cell neuroendocrine carcinoma (LCNEC).

  Study JGAS000903

• Plasma cell-free small RNA sequencing comparison of Illumina and Ultima Genomics in pancreatic cancer

  This study evaluates plasma-derived cell-free small RNA sequencing in a case-control cohort of 39 pancreatic ductal adenocarcinoma patients and 39 control subjects. Cell-free nucleic acids were isolated from plasma, small RNA libraries were prepared using the SMARTer smRNA-Seq workflow, and the same libraries were sequenced on both Illumina NovaSeq 6000 and Ultima Genomics UG 100 platforms. The project aims to compare platform performance for circulating small RNA and mature microRNA profiling, including read yield, alignment, RNA biotype composition, mature miRNA detection, expression concordance, and pancreatic cancer-associated differential expression. The dataset supports benchmarking of cross-platform reproducibility and assessment of biologically relevant plasma microRNA signals in pancreatic cancer.

  Study EGAS50000001907

• Plasma cell-free small RNA sequencing of pancreatic cancer and control samples

  This dataset contains plasma-derived cell-free small RNA sequencing data from 78 human samples, comprising 39 pancreatic ductal adenocarcinoma patients and 39 control subjects. Each sample was sequenced as a single-end FASTQ library on both Illumina NovaSeq 6000 and Ultima Genomics UG 100 platforms, yielding 156 submitted sequencing runs. The data were generated from SMARTer smRNA-Seq libraries to support comparative analysis of sequencing performance, mature microRNA detection, and pancreatic cancer-associated cell-free small RNA expression across the two platforms.

  Dataset EGAD50000002771

• VIVO Biobank Neuroblastoma Project Data Accession Committee

  Access to neuroblastoma sample data for VIVO Biobank projects.

  Dac EGAC50000000977

• Frequency and prognostic significance of genetic abnormalities in a subgroup of intermediate risk neuroblastoma patients: A SIOPEN study

  Intermediate risk neuroblastoma includes a rare subgroup of patients over the age of 18 months at diagnosis with unresectable, localised tumours with unfavourable histology without MYCN amplification. This group is treated differently around the world with some patients receiving upfront myeloablative therapy (MAT) in addition to standard treatment with chemotherapy, surgery, radiotherapy and 13 cis retinoic acid, but the overall 5-year patient survival with or without MAT is similar (75%). This study aimed to understand the neuroblastoma biology of this rare patient subgroup.

  Study EGAS50000001908

• TNGS data for SIOPEN intermediate-risk neuroblastoma subgroup study

  Targeted next-generation sequencing data for a SIOPEN intermediate-risk neuroblastoma subgroup (age >18 months, localised, unresectable, non-MYCN-amplified neuroblastoma with unfavourable histology), with a total of 31 cases analysed. All but one sample have been analysed using the same workflow, with the exception being NB32. VCF files for the analyses are included in this dataset.

  Dataset EGAD50000002773

• WES data for SIOPEN intermediate-risk neuroblastoma subgroup study

  Whole exome sequencing (WES) data analysed for the SIOPEN intermediate-risk neuroblastoma subgroup (age >18 months, localised, unresectable, non-MYCN amplified neuroblastoma with unfavourable histology), data for a total of 30 cases available. Four samples were analysed as part of another relapse neuroblastoma study (NB02 to NB05), of which two have germline comparison available. The remaining 26 cases had a different analysis workflow, with all but one having germline comparison available. VCF files from WES analyses are included in this dataset.

  Dataset EGAD50000002774

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma

• The last addition to the list of clonal evolution studies in the EGA

  Tumours of different types may arise from a single cell with at least one malignant mutation. Accumulation of subsequent hits generates lineages of cells with different cancer transformation power; thus, every clone follows an autonomous evolution, and several clones may co-exist into the same solid or liquid tumour. Clonal evolution studies are not new: the conceptual power of this approach has been recognized since the early ’80, especially in leukaemia patients. Nonetheless, these studies greatly benefit from recent technological improvements -machineries and protocols-, which enable ever-increasing sequencing depth, allowing the detection of low frequency variants and consequently more tumour clones. Note: the figure belongs to the original paper and is owned by the authors. Researchers at the University of Kyoto in Japan, have indeed used state of the art knowledge to investigate the clonal evolution of chronic myeloid leukemia (CML) in a large panel of patients. CML progresses from a majoritarian benign chronic phase into an often fatal blast crisis phase. It is nowadays known that blast crisis are triggered by genetic instability and disrupted DNA repair, which lead to accumulation of DNA mutations. The use of thyrosine kinase inhibitors targeting the Philadelphia chromosome have enormously improved the treatment of blast crisis, nonetheless resistant patients are still a medical challenge. Yotaro Ochi and collegues used Whole Exome Sequencing of paired chronic phase and blast crisis samples to depict the genetic landscape of progression from the first to the second stage of the disease (as illustrated in their figure). Their results were published in Nature Communication on this May 2021. They used a dataset previously deposited at the EGA (EGAS00001003071) and their new generated data have been made available under accession number EGAS00001005075. Their analysis of the occurring mutations contribute to understanding the mechanisms of CML evolution and therefore develop a protocol for early management of patients with a TKI resistance signature. Up to date, the EGA hosts 99 clonal evolution studies, developed in several cancer types, including myeloma, renal carcinomas and ovarian cancer, in addition to various leukaemia subtypes, where this approach has been more widely applied and produced important results and seminal papers. From a pan-cancer point of view, the definition of tumour architecture through clonal evolution studies is instrumental to unravel the mechanisms of disease progression and treatment-resistance insource. At the EGA, we are honoured to contribute to exciting and important science advances with our service!

  Blog the-last-addition-to-the-list-of-clonal-evolution-studies-in-the-ega