8224 results for "canc*"

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• Follicular Lymphoma Whole Genome and Transcriptome Sequencing

  Study EGAS00001006646

• Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens

  Study EGAS00001007404

• METABRIC

  Solid tumors are complex tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Normal cell contamination can dilute cancer cell information and tissue architecture is generally not reflected in molecular assays. To address these challenges, we developed a computational approach based on standard Haematoxylin and Eosin-stained sections and demonstrated its power in a discovery cohort of 323 breast tumors and an independent validation cohort of 241 tumors. First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.

  Study EGAS00000000098

• Efficacy of dual KRAS G12D–EGFR blockade versus triple combinations in patient-derived models of KRAS G12D-mutant colorectal cancer

  RNA sequencing was performed on patient-derived xenograft (PDX)-derived organoid models of metastatic colorectal cancer (mCRC). The aim of this study was to characterize the transcriptional landscape of KRAS-mutant mCRC cells in response to therapeutic strategies targeting the EGFR and KRAS signaling pathways. Transcriptomic profiling was conducted following drug treatments to investigate molecular changes associated with single and combination therapies. A total of 24 samples were analyzed.

  Study EGAS50000001700

• Genetic and evolutionary patterns of treatment resistance in relapsed B-cell lymphoma

  Study EGAS00001004469

• IDH1 Somatic Mutation Profile in Intrahepatic Cholangiocarcinoma

  The objective of this study is to identify somatic IDH1 mutations in intrahepatic cholangiocarcinoma samples.

  Study EGAS50000001638

• Whole-genome sequencing of cell-free DNA from pancreatic and breast cancer cohorts for fragmentomic and tumor fraction analysis

  This project provides whole-genome sequencing data from cell-free DNA collected from patients with pancreatic ductal adenocarcinoma and breast cancer, alongside healthy and benign controls. The study aims to identify novel liquid biopsy biomarkers by exploring the relationship between per-base quality scores and fragmentomic signals like end-motifs and fragment length. Furthermore, the data supports the development of Bayesian deep learning frameworks for the uncertainty-aware estimation of tumor fraction and ploidy in low-concentration clinical samples.

  Study EGAS50000001620

• High-grade B-cell lymphoma, not otherwise specified: an LLMPP study

  Molecular characterization of high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), is hindered by its rarity, evolving definition, and poor diagnostic reproducibility. To address this challenge, we analyzed 92 HGBCL-NOS tumors collected across Lymphoma/Leukemia Molecular Profiling Project sites. These tumors were profiled using whole-genome sequencing (WGS) or whole-exome sequencing (WES) to identify somatic mutations, WGS or low-pass WGS to detect copy number alterations, targeted capture sequencing to assess MYC and BCL2 structural variants, and RNA sequencing. The genetic features of HGBCL-NOS were compared to those of diffuse large B-cell lymphoma (DLBCL-NOS) and EBV-negative adult Burkitt lymphoma (BL). DLBCL-NOS tumors underwent targeted capture sequencing using a panel of 125 genes recurrently mutated in aggressive B-cell lymphomas. BL biopsies were drawn from the Burkitt Lymphoma Genome Sequencing Project and had available WGS (dbGaP Study Accession: phs000527.v18.p6).

  Study EGAS50000000932

• Patient-derived organoids as predictive models for drug testing and repurposing in glioblastoma therapy

  Glioblastoma multiforme (GBM) remains a highly aggressive brain tumor with limited treatment options and poor prognosis. Temozolomide (TMZ) is the only approved first-line therapy, but frequent resistance limits its efficacy, highlighting the urgent need for alternative treatments. Patient-derived glioblastoma organoids (GBOs) offer a promising preclinical model for personalized drug testing and therapy development.

  Study EGAS50000001221

• Tumoral immune microenvironment profiling in B-cell non-Hodgkin lymphoma stratifies patients in different prognostic groups

  Non-Hodgkin lymphomas (NHL) are heterogeneous B-cell malignancies in which the tumor immune microenvironment (TIM) influences disease progression and has emerging potential for prognostic patient stratification. This project investigates the TIM of NHL by combining multiparameter flow cytometry with targeted DNA sequencing of NHL biopsies. The study aimed to characterize the immune cell composition, immune checkpoint expression, and mutational landscape, and to evaluate their association with clinical and prognostic features across NHL subtypes. The classification of TIM profiles by flow cytometry predicts clinical outcomes in patients with follicular lymphoma (FL), highlighting its potential value for risk stratification and the identification of novel immunotherapeutic targets in NHL.

  Study EGAS50000001964