5731 results for "canc*"

in 18.80 milliseconds.

• Machine learning to detect the SINEs of cancer

  We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA (cfDNA) through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a novel machine-learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to samples from 5108 individuals, 2037 with cancer and the remainder without cancer. Samples from cancer patients and controls were pre-specified into four cohorts used for: 1) model training, 2) analyte integration and threshold determination, 3) validation, and 4) reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the Validation Cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and 8 common protein biomarkers detected 51% of 1167 cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature – the global reduction of AluS sub-family elements in the circulating DNA of cancer patients. We confirmed this reduction through the analysis of another independent dataset obtained with a very different approach (whole genome sequencing). The evaluation of Alu elements therefore has the potential to enhance the performance of several methods designed for the earlier detection of cancer.

  Study EGAS00001007169

• Time series analysis of neoadjuvant chemotherapy and bevacizumab treated breast carcinomas reveals a systemic shift in genomic aberrations

  Study EGAS00001003287

• First in vivo investigation of the impact of PARP inhibition with rucaparib alone and in combination with atezolizumab: results of the phase Ib COUPLET clinical study in advanced gynecologic and triple-negative breast cancers

  Study EGAS00001006100

• NOWAC blood-breast cancer Data Access Committee

  Dac EGAC01000000024

• PD-L1 blockade immunotherapy rewires cancer-induced emergency myelopoiesis

  Dac EGAC00001003503

• Seq-ing the SINEs of Central Nervous System Tumors in Cerebrospinal Fluid

  It is often challenging to distinguish cancerous from non-cancerous lesions in the brain using conventional diagnostic approaches. We introduce an analytic technique called Real-CSF to detect cancers of the central nervous system from evaluation of DNA in the cerebrospinal fluid (CSF). Short interspersed nuclear elements (SINEs) are PCR-amplified with a single primer pair and the PCR products are evaluated by next generation sequencing. Real-CSF assesses genome wide copy number alterations as well as focal amplifications of selected oncogenes. Real-CSF was applied to 280 CSF samples and correctly identified 67% of 184 cancers and 96% of 96 non-cancerous brain lesions. CSF analysis was considerably more sensitive than standard of care cytology and plasma cell-free DNA analysis in the same patients. Real-CSF therefore has the capacity to be used in combination with other clinical, radiologic, and laboratory-based data to inform the diagnosis and management of patients with suspected cancers of the brain.

  Study EGAS00001007401

• Genomic and anti-tumor immune evolution of breast cancer

  Study EGAS00001004956

• Sequencing of Breast Cancer

  Study EGAS00001007490

• Sequencing of Ovarian Cancer

  Study EGAS00001007489

• Mutant p53 confers gain-of-function transcriptional activity in liver cancer

  Mutant p53 confers gain-of-function transcriptional activity in liver cancer

  Study EGAS00001005779