15 research outputs found

    Genomic context of NTRK1/2/3 fusion-positive tumours from a large real-world population

    Get PDF
    Abstract Neurotrophic tropomyosin receptor kinase (NTRK) gene fusions are rare oncogenic drivers in solid tumours. This study aimed to interrogate a large real-world database of comprehensive genomic profiling data to describe the genomic landscape and prevalence of NTRK gene fusions. NTRK fusion-positive tumours were identified from the FoundationCORE® database of >295,000 cancer patients. We investigated the prevalence and concomitant genomic landscape of NTRK fusions, predicted patient ancestry and compared the FoundationCORE cohort with entrectinib clinical trial cohorts (ALKA-372-001 [EudraCT 2012-000148-88]; STARTRK-1 [NCT02097810]; STARTRK-2 [NCT02568267]). Overall NTRK fusion-positive tumour prevalence was 0.30% among 45 cancers with 88 unique fusion partner pairs, of which 66% were previously unreported. Across all cases, prevalence was 0.28% and 1.34% in patients aged ≥18 and <18 years, respectively; prevalence was highest in patients <5 years (2.28%). The highest prevalence of NTRK fusions was observed in salivary gland tumours (2.62%). Presence of NTRK gene fusions did not correlate with other clinically actionable biomarkers; there was no co-occurrence with known oncogenic drivers in breast, or colorectal cancer (CRC). However, in CRC, NTRK fusion-positivity was associated with spontaneous microsatellite instability (MSI); in this MSI CRC subset, mutual exclusivity with BRAF mutations was observed. NTRK fusion-positive tumour types had similar frequencies in FoundationCORE and entrectinib clinical trials. NTRK gene fusion prevalence varied greatly by age, cancer type and histology. Interrogating large datasets drives better understanding of the characteristics of very rare molecular subgroups of cancer and allows identification of genomic patterns and previously unreported fusion partners not evident in smaller datasets

    Generation of a reference transcriptome for evaluating rainbow trout responses to various stressors

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Fish under intensive culture conditions are exposed to a variety of acute and chronic stressors, including high rearing densities, sub-optimal water quality, and severe thermal fluctuations. Such stressors are inherent in aquaculture production and can induce physiological responses with adverse effects on traits important to producers and consumers, including those associated with growth, nutrition, reproduction, immune response, and fillet quality. Understanding and monitoring the biological mechanisms underlying stress responses will facilitate alleviating their negative effects through selective breeding and changes in management practices, resulting in improved animal welfare and production efficiency.</p> <p>Results</p> <p>Physiological responses to five treatments associated with stress were characterized by measuring plasma lysozyme activity, glucose, lactate, chloride, and cortisol concentrations, in addition to stress-associated transcripts by quantitative PCR. Results indicate that the fish had significant stressor-specific changes in their physiological conditions. Sequencing of a pooled normalized transcriptome library created from gill, brain, liver, spleen, kidney and muscle RNA of control and stressed fish produced 3,160,306 expressed sequence tags which were assembled and annotated. SNP discovery resulted in identification of ~58,000 putative single nucleotide polymorphisms including 24,479 which were predicted to fall within exons. Of these, 4907 were predicted to occupy the first position of a codon and 4110 the second, increasing the probability to impact amino acid sequence variation and potentially gene function.</p> <p>Conclusion</p> <p>We have generated and characterized a reference transcriptome for rainbow trout that represents multiple tissues responding to multiple stressors common to aquaculture production environments. This resource compliments existing public transcriptome data and will facilitate approaches aiming to evaluate gene expression associated with stress in this species.</p

    Deep learning is combined with massive-scale citizen science to improve large-scale image classification

    No full text
    Pattern recognition and classification of images are key challenges throughout the life sciences. We combined two approaches for large-scale classification of fluorescence microscopy images. First, using the publicly available data set from the Cell Atlas of the Human Protein Atlas (HPA), we integrated an image-classification task into a mainstream video game (EVE Online) as a mini-game, named Project Discovery. Participation by 322,006 gamers over 1 year provided nearly 33 million classifications of subcellular localization patterns, including patterns that were not previously annotated by the HPA. Second, we used deep learning to build an automated Localization Cellular Annotation Tool (Loc-CAT). This tool classifies proteins into 29 subcellular localization patterns and can deal efficiently with multi-localization proteins, performing robustly across different cell types. Combining the annotations of gamers and deep learning, we applied transfer learning to create a boosted learner that can characterize subcellular protein distribution with F1 score of 0.72. We found that engaging players of commercial computer games provided data that augmented deep learning and enabled scalable and readily improved image classification.QC 20181001</p
    corecore