5735 results for "canc*"

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• Evaluation of somatic mutations in urine samples as a non-invasive method for the detection and molecular classification of endometrial cancer

  Purpose: Current diagnostic methods for endometrial cancer lack specificity, leading to many women undergoing invasive procedures. The aim of this study was to evaluate somatic mutations in urine to accurately discriminate endometrial cancer patients from controls. Experimental Design: Overall, 72 samples were analyzed using next-generation sequencing with molecular identifiers targeting 47 genes. We evaluated urine supernatant samples from women with endometrial cancer (n=19) and age-matched controls (n=20). Cell pellets from urine and plasma samples from seven cases were sequenced; further, we also evaluated paired tumor samples from all cases. Finally, immunohistochemical markers for molecular profiling were evaluated in all tumor samples. Results: Overall, we were able to identify mutations in DNA from urine supernatant samples in 100% of endometrial cancers. In contrast, only one control (5%) showed variants at a variant allele frequency (VAF)≥2% in the urine supernatant samples. The molecular classification obtained by using tumor samples and urine samples showed good agreement. Analyses in paired samples revealed a higher number of mutations and VAFs in urine supernatants than in urine cell pellets and blood samples. Conclusions: Evaluation of somatic mutations using urine samples may offer a user-friendly and reliable tool for endometrial cancer detection and molecular classification. The diagnostic performance for endometrial cancer detection was very high, and cases could be molecularly classified using these noninvasive and self-collected samples. Additional multi-center evaluations using larger sample sizes are needed to validate the results and understand the potential of urine samples for the early detection and prognosis of endometrial cancer.

  Study EGAS00001007396

• SYNTHETIC: EOSC4Cancer Study Synthetic Colorectal Cancer Genomic data

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) is utilized to synthetize these 10 pairs tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) are learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aims to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient is down-sampled randomly, which includes 50% germline variants of such a patient. It is then mixed together with an in silico germline variant profile that is modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learn from a real CRC patient is added to the germline variant profile used for creating normal synthetic WGS data of the same patient, which is utilized to produce simulated sequences. Neither copy number profile nor structural variation profile is introduced into the tumor synthetic WGS data. Tumor content and ploidy are assumed to be 100% and 2.

  Study EGAS50000000386

• Genomic and Transcriptomic Profiling of Colorectal Cancer in Swedish Patients

  My descriptionThis study investigates the genomic and transcriptomic landscape of colorectal cancer (CRC) in Swedish patients to identify molecular biomarkers associated with disease progression, treatment response, and survival outcomes. A total of 350 patients diagnosed with CRC between 2018 and 2023 were recruited from three Swedish hospitals: Lund University Hospital, Karolinska University Hospital, and Sahlgrenska University Hospital. Matched tumor tissue and blood samples were collected from all participants after obtaining informed consent. Whole-genome sequencing (WGS) was performed on blood-derived DNA to identify germline variants, while RNA-seq analysis of tumor tissues provided transcriptomic profiles. In addition, targeted panel sequencing was carried out for selected cancer-related genes to confirm findings from the WGS data.

  Study EGAS50000000158

• EGAD00000000013

  WTCCC1 project Breast cancer (BC) samples

  Dataset EGAD00000000013

• EGAD00000000048

  Sequencing data from oestrogen-receptor-alpha-positive metastatic lobular breast cancer sample

  Dataset EGAD00000000048

• EGAD00000000049

  RNA-SEQ data from oestrogen-receptor-alpha-positive metastatic lobular breast cancer sample

  Dataset EGAD00000000049

• EGAD00000000053

  Sequencing data from Breast Cancer samples

  Dataset EGAD00000000053

• EGAD00000000054

  NCI-H209 is an immortal cell line derived from a bone marrow metastasis of a patient with small cell lung cancer, taken before chemotherapy. The specimen showed histologically typical small cells with classic neuroendocrine features. NCI-BL209 is an EBV-transformed B-cell line derived from the same patient as the small cell lung cancer cell line, NCI-H209

  Dataset EGAD00000000054

• COLO-829/COLO-829BL

  COLO-829 is a publicly available immortal cancer cell line and COLO-829BL is a lymphoblastoid cell line derived from the same patient

  Dataset EGAD00000000055

• EGAD00010000622

  SNP array data for gastric cancer cell lines

  Dataset EGAD00010000622

• EGAD00010000644

  Affymetrix SNP6.0 cancer cell line exome sequencing data

  Dataset EGAD00010000644

• EGAD00010000266

  Metabric breast cancer samples (Genotype raw data)

  Dataset EGAD00010000266

• EGAD00010000268

  Metabric breast cancer samples (Expression raw data)

  Dataset EGAD00010000268

• EGAD00010000270

  Metabric breast cancer samples (Images)

  Dataset EGAD00010000270

• EGAD00010000474

  blood-based gene expression from breast cancer cases and age-matched controls in case-control serie 2 (CC2)

  Dataset EGAD00010000474

• EGAD00010000476

  blood-based gene expression from breast cancer cases and age-matched controls in case-control serie 1 (CC1)

  Dataset EGAD00010000476

• EGAD00010000478

  blood-based gene expression from breast cancer cases and age-matched controls in case-control serie 3 (CC3)

  Dataset EGAD00010000478

• EGAD00010000498

  Affymetrix SNP6.0 genotype data for prostate cancer patients

  Dataset EGAD00010000498

• EGAD00010000526

  SNP 6.0 arrays of small cell lung cancer

  Dataset EGAD00010000526

• EGAD00010000554

  SNP 6.0 arrays of small cell lung cancer

  Dataset EGAD00010000554

• EGAD00010000556

  SNP 6.0 arrays of small cell lung cancer

  Dataset EGAD00010000556

• EGAD00010000558

  SNP 6.0 arrays of small cell lung cancer

  Dataset EGAD00010000558

• CLL cancer Sample Sequencing

  CLL cancer Sample Sequencing

  Dataset EGAD00001000004

• Various Cancer Fusion Gene Sequencing

  Various Cancer Fusion Gene Sequencing

  Dataset EGAD00001000005

• Renal Cancer Exome Sequencing

  Agilent whole exome hybridisation capture will be performed on genomic DNA derived from 25 renal cancers and matched normal DNA from the same patients. Three lanes of Illumina GA sequencing will be performed on the resulting 50 exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes.

  Dataset EGAD00001000014