7792 results for "canc*"

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• Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma

  Background: Cancer patients with advanced disease exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates sequencing data with functional assay data to develop patient-specific combination cancer treatments. Methods: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient’s epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient’s primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. Results Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). Conclusions These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.

  Study EGAS00001003564

• Analysis of four key cell types (epithelial, fbroblast, myeloid and T cells)

  Alterations of the normal prostate epithelial cell genome trigger the emergence of carcinoma, however cancer-only analyses of genomic aberrations have resulted in little clinical translation. A deep understanding of the tumor microenvironment is likely to inform on key drivers of disease progression and novel therapeutic opportunities. We investigated the role of key cell types along disease progression, profiling the RNA abundance of 52 samples from 13 patients enriched for one of four key cell types, spanning a wide CAPRA-S risk score range. Using a novel differential transcription Bayes method, TABI, we avoided a priori patient stratification, and mapped transcriptional alteration events to specific risk scores. The developmental and cell-type resolution yielded a landscape of concurrent transcriptional alterations from the tumour microenvironment, including key hallmarks. These transcriptional signatures permitted the identification of a “high survival” patient cluster in an independent prostate cancer dataset. Most importantly, together with our integrated transcriptional analysis, differential tissue composition analysis provided evidence for a key role for monocytes and macrophages in prostate cancer progression and disease recurrence.

  Study EGAS00001003579

• Genome-wide cell-free DNA fragmentation in patients with cancer

  Cell-free DNA (cfDNA) in the blood provides a noninvasive diagnostic avenue for patients with cancer. However, characteristics of the origins and molecular features of cfDNA are poorly understood. We developed an approach to evaluate fragmentation patterns of cfDNA across the genome and found that cfDNA profiles of healthy individuals reflected nucleosomal patterns of white blood cells, while patients with cancer had altered fragmentation profiles. We applied this method to analyze fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers and 245 healthy individuals. A machine learning model incorporating genome-wide fragmentation features had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity, with an overall AUC of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation based cfDNA analyses detected 91% of cancer patients. The results of these analyses highlight important properties of cfDNA and provide a proof of principle approach for screening, early detection, and monitoring of human cancer.

  Study EGAS00001003611

• Balanced_Ependymoma

  We propose to definitively characterise the somatic genetics of a number of pediatric malignant tumours including ependymoma, high grade glioma and central nervous system primitive neurectodermal tumours through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing.

  Study EGAS00001000174

• Genetic landscape of pediatric ependymoma

  We performed whole-genome sequencing of 42 paired tumor/normal EPD genomes and analyzed somatically acquired single nucleotide variations (SNVs), insertion/deletions, structural variations and copy number variations in the cancer genomes

  Study EGAS00001000254

• AML_targeted_resequencing_study

  Genomic libraries will be generated from whole genome amplified genomic DNA derived from 3,000 myeloid neoplasms. Targeted capture will be performed by multiplexing 16 barcoded samples in each library. Pools of 96 DNA samples will be subjected to 1-2 lanes of HiSeq 75bp sequencing. Reads will be mapped to the current build of the human reference genome to facilitate the generation of a comprehensive catalog of somatic mutations associated with 116 known myeloid and cancer driver genes.

  Study EGAS00001000275

• 2017_AML_WGS

  This 2017_AML_WGS study includes 10 paired samples derived from Korean AML paitients. 10 AML Bone Marrow samples were collected and paired with each normal(Saliva or BM derived) sequencing data. The results of this WGS study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001002388

• Feasibility_of_targeted_capture_sequencing_in_routinely_collected_FFPE_cancer_specimens

  Testing the feasibility of genome-scale sequencing in routinely collected formalin-fixed paraffin-embedded (FFPE) cancer specimens versus matched fresh-frozen samples using targeted pulldown capture prior to Illumina sequencing.

  Study EGAS00001000297

• RNA-sequençing of 21 inflammatory hepatocellular adenomas

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors.The present series corresponds to 21 RNA-seq of IHCA samples. Aligned bam files correspond to hg38 human reference.

  Study EGAS00001003685

• Myeloma_Targeted_Follow_up_Study

  390 samples, perform WGA, and send the WGA’d DNA to core for pre-capture library and tagging, pulldown with ELID 631172 (16 samples per reaction tube), and sequencing (24 samples per lane)

  Study EGAS00001000880