7411 results for "canc*"

in 20.33 milliseconds.

• Multi-Region WES of Metastatic Colorectal Cancer

  In order to reconstruct the evolutionary history of metastatic colorectal cancer, we performed whole-exome sequencing of 12 metastatic colorectal cancer patients for whom the primary tumor and matched distant metastases to the brain (n=10) and liver (n=2). For 8 of the 12 patients, multiple regions (n=3-7) of the primary tumor and distant metastases were sequenced.

  Dataset EGAD00001004896

• Genome and transcriptome sequence data from a locally advanced breast cancer patient

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

  Dataset EGAD00001004897

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

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

  Dataset EGAD00001004898

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

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

  Dataset EGAD00001004899

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

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

  Dataset EGAD00001004900

• MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGS)

  B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the genetic landscape of this particular leukemia subtype, we performed whole genome DNA- and B-cell receptor (BCR)- on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

  Dataset EGAD00001005017

• MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGBS)

  B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the epigenetic landscape of this particular leukemia subtype, we performed DNA bisulfite-sequencing on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

  Dataset EGAD00001005018

• Clonality of circulating tumor cells in breast cancer brain metastases patients

  The incidence of brain metastases in breast cancer (BCBM) patients is increasing. These patients have a very poor prognosis and therefore identification of blood-based biomarkers, such as circulating tumor cells (CTCs) and understanding the genomic heterogeneity could help to personalize treatment options. In this study, DNA from individual CTCs as well as corresponding primary tumors and brain metastases were analyzed by next generation sequencing (NGS) in order to evaluate copy number aberrations and single nucleotide variations (SNVs).

  Dataset EGAD00001005020

• A practical guide for mutational signature analysis in hematological malignancies

  Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

  Dataset EGAD00001005028

• Genomic Signatures Reveal DNA Damage Response Deficiency in Brain Metastases of Colorectal Cancer

  Brain metastasis (BM) of colorectal cancer (CRC) is rare but lethal and lacks effective therapies or a good understanding of its genomic landscapes. We conduct an analysis of whole-exome sequencing (WES, Illumina HiSeq 2500 sequencing platform) on 11 patient-matched BMs, primary CRC tumours, and adjacent normal tissues; and whole-genome sequencing (WGS, Illumina HiSeq X Ten platform) on 8 patient-matched BMs, primary CRC tumors, and adjacent normal tissues to uncover the whole-genome mutational landscape of colorectal cancer with brain metastasis.

  Dataset EGAD00001005030