7363 results for "canc*"

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• Evaluation_of_size_selection_on_cancer_specific_sequencing_libraries

  A couple of previously characterized and sequenced libraries will be repeated using a couple of differing size selection criteria and skim sequenced using an Illumina HiSeq. The resulting sequence will be analyzed to determine the optimal DNA library size for our specific downstream analysis.

  Study EGAS00001000293

• Osteosarcoma_X10

  This is a continuation of the osteosarcoma work we have undertaken.

  Study EGAS00001002167

• Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection

  Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. We Here we developed and validated a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyzed whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a newly developed bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.

  Study EGAS00001003206

• Sequencing data from MethylScan assay on plasma

  Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic influences. Here, we present DeBias, a computational framework for mitigating demographic biases in high-dimensional biomedical datasets. DeBias eliminates bias-associated subspaces within the entire feature space using control samples and enables global bias correction across all features, effectively preserving disease-specific signals while minimizing demographic distortions. To demonstrate its effectiveness, we applied DeBias to cfDNA methylation data for cancer detection. It achieved a >6-fold reduction in the number of features containing biases and outperformed existing methods in improving the cancer detection performance for minority populations. Our method represented a promising step toward developing machine learning models that are more accurate, reliable, and equitable for minority populations.

  Study EGAS00001008127

• Myeloproliferative_Neoplasms__MPN__Exome_Validation_Study

  Experiments using targeted pulldown methods will be sequenced to validate findings in the exomes of patients with Myeloproliferative Neoplasms (MPN).

  Study EGAS00001000404

• Oncogenic fate conversion by PRDM16s causes acute myeloid leukemia

  Oncogenic mutations confer aberrant replicative capacity to cells with little or no replicative capacity, generating cancer stem cells that perpetuate the tumor through extensive self-replication and differentiation blockade. However, whether oncogenes disrupt the cellular identity ofcancer stem cell by altering the developmental potential is unknown. Fate conversion has been demonstrated by ectopic expression of master transcriptional regulators, such as PU.1 and C/EBP alpha that confers myeloid cell fate to other cell types, and PRDM16 that confers brown adipose fate to white adipocytes or myoblasts. Here we show that a transcriptional regulator overexpressed in human myeloid malignancies, PRDM16s, causes oncogenic fate conversion, by transforming cells fated to form platelets and erythrocytes into myeloid leukemia-initiating cells (LICs). Prdm16s expression in hematopoietic progenitor cells caused a myelodysplastic syndrome (MDS)-like disease that progressed to acute myelogenous leukemia (AML). The myeloid diseases caused by Prdm16s exhibited expansion of megakaryocyte-erythroid progenitors (MEPs) but not granulocyte-macrophage progenitors. MEPs from Prdm16s-induced leukemia possessed LIC potential, and expression of Prdm16s in normal MEPs was sufficient to convert them to myeloid LICs and blocked megakaryocytic/erythroid potential. Prdm16s induced the expression of myeloid master regulators, including PU.1 and C/EBP alpha, by interacting with their super enhancers. Ablation of myeloid master regulators attenuated the myeloid potential and reinstalled the megakaryocytic/erythroid potential of leukemic-MEPs in mouse models and in human AML with PRDM16 rearrangement. Our study demonstrates that oncogenic Prdm16 expression converts the fate of MEPs to a malignant myeloid fate by activating myeloid master transcription factors.

  Study EGAS00001003235

• 1 Intratumoral genetic heterogeneity and clonal evolution following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal tumors.

  Purpose: Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment forpatients with locally advanced rectal cancer. However, little is known about the effect of nCRT in the mutational landscape and intratumoral genetic heterogeneity (ITGH) of rectal tumors. Moreover, response to nCRT vary substantially among patients and the genetic determinants of treatment response are not fully understood. Here, we sought to determine the impact of nCRT in the mutational landscape and ITGH of rectal tumors and to identify putative drivers of therapeutic resistance. Patients and Methods: We analyzed whole-exome sequencing (WES) and clinical data from 79 primary non-treated rectal cancers obtained from The Cancer Genome Atlas (TCGA). We also compared the mutational landscape of a matched set of peripheral blood cells, irradiated tumor-adjacent colonic mucosa and pre and post-treatment tumor samples to determine the iatrogenic effects of nCRT and its impact in tissue genetic heterogeneity. Finally, we performed WES of 7 matched pre and post-treatment tumor samples to examine how nCRT affects the clonal architecture of rectal tumors. Results: We show that rectal tumors exhibit remarkable ITGH, which increases with disease progression. We also show that nCRT, per se, does not introduce novel somatic mutations, nor alters the clonal architecture of the irradiated colonic mucosa. Instead, we show that it acts as a potent selective pressure, favoring the expansion of tumor cell subpopulations more prone to resist therapy and increasing ITGH in the residual tumor. Conclusion: Our results reveal the highly heterogeneous and dynamic clonal architecture of rectal tumors undergoing nCRT. Our results also uncover putative genetic determinants of response to nCRT and a possible role for ITGH in predicting treatment outcome.

  Study EGAS00001003250

• Discovery of new fusion transcripts in a cohort of pediatric solid cancers at relapse

  This study aims to dissect further the complexity of fusion landscape in recurrent pediatric cancers. A retrospective study on RNA-sequencing data of 48 pediatric patients at relapse with different malignancies was conducted in order to detect and validate several fusions and predict their probable oncogenic potential and targetability. We obtained a high validation rate of 90% of selected candidates which will serve both clinical and research needs to detect and prioritize experimental validation studies leading to the development of new therapeutic options.

  Study EGAS00001003236

• Breast_cancer_sequential_sampling_study

  This study aims to whole genome sequence DNA derived from breast cancer patients who received neo-adjuvany chemotherapy. All patients had multiple biopsies performed before chemotherapy. Patients who had residual disease after the course of treatment underwent a further biopsy. We aim to characterise the mutations involved.

  Study EGAS00001000300

• LICA-CN project - 116 liver cancer cases

  LICA-CN project - 116 liver cancer cases

  Study EGAS00001002300