• Login
    View Item 
    •   Home
    • Events
    • Digital Health 2020
    • View Item
    •   Home
    • Events
    • Digital Health 2020
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    CAN-VP: CANcer Variant Prioritization

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Poster
    Authors
    Althubaiti, Sara cc
    Gkoutos, Georgios cc
    Hoehndorf, Robert cc
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-1-20
    Permanent link to this record
    http://hdl.handle.net/10754/661209
    
    Metadata
    Show full item record
    Abstract
    Introduction Identifying and prioritizing driver mutations that play main role to develop cancer still a  major challenge. Several computational approaches involved machine learning and statistical methods exist to access finding these driver mutations depending on pre-computed pathogenicity scores derived from different tools. We have developed CANcerVariant Prioritization (CAN-VP) system to identify and prioritize driver mutations. Ourtool exploits the background knowledge behind using different ontologies that utilize cellular phenotypes, functions, and whole-body physiological phenotypes besides combining region-based information as features. We demonstrate the performance of CAN-VP in prioritizing causative driver mutations on a number of synthetic whole exome from The  Cancer Genome Atlas (TCGA), targeting 4 different primary sites. We find that CAN-VP could identify most of the causative driver mutations compared to the existing tools which showed its capability as a tool for discovering driver mutations. Methods and Materials Data sources We relied on two main types of datasets, first one is from well-known cancer-related databases such as:  COSMIC1, CanProVar2, IntOGen3. The second one is the real samples included in The Cancer Genome Atlas (TCGA)4 which involve more than 60 different projects covering 67 primary sites;  but till now we focus on 4 projects (Sarcoma,  Kidney, Lung, and Bladder). Moreover, we used the 579 validated driver mutations in Bailey, Matthew H., etal5. Results and Discussion 1. Prediction model 1.1 Model details We implemented our CAN-VP using a fully connected neural network model in Python 3.6 as shown in Figure 4. We used Keraswith a TensorFlow backend. We ignored the missing values for all the features being used. We added additional flags for missing values as features.  We retrieved genes embeddings from and used them as features in the prediction model. 1.2 Training and testing data We downloaded COSMIC mutations VCF file on 26th Jul, 2019.  It includes 4,788,121cancer mutations.  We also downloaded DoCMdataset as a VCF file on 18th Nov, 2019. It includes 1364 curated driver mutations.  Moreover, we downloaded CanProVaras afastqfile on 18th Nov, 2019.  It includes 156,671 driver mutations. Based on that, we tried to find how much mutations of DoCM+ CanProVarexist within COSMIC and consider them as positives; otherwise, they would be negatives. As Table 1 showed, the number of negatives data (unknown driver somatic mutations) are much more than the positive ones (validated as driver mutations). 1.3 Prediction performance We trained our model in Figure 2 using the dataset in Table 1 and do the testing on the synthetic datasets. The updated results of CAN-VP compared to the other tools are shown in Table 2. In terms of evaluating the importance of different features in our prediction model, we first test the different combinations of features from CanDrAwhich includes (86 from CHASMplus and 3 from Mutation Assessor) plus 3 from UCSC. Moreover, we add the gene embeddings and the results become better by 3%. Table 3summaries the performance for each experiment. Future Work - Test CAN-VP on much comprehensive cancer-related datasets. - Integrate graph-basedfeaturestoCAN-VP model. References 1SallyBamford et al. “The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. In: British journal of cancer 91.2 (2004), p. 355.   2 Jing Li, Dexter T Duncan, and Bing Zhang. “CanProVar: a human cancer proteome variation database. In: Human mutation 31.3 (2010), pp. 219–228.   3 GunesGundemet al. “IntOGen: integration and data mining of multidimensional oncogenomic data. In: Nature methods 7.2 (2010), p. 92.   4 Katarzyna Tomczak, Patrycja Czerwínska, andMaciejWiznerowicz. “The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. In: Contemporaryoncology19.1A(2015), A68.   5 Matthew H Bailey et al. “Comprehensive characterization of cancer driver genes and mutations. In: Cell173.2(2018), pp. 371–385.
    Conference/Event name
    Digital Health 2020
    Additional Links
    https://epostersonline.com//dh2020/node/58
    Collections
    Digital Health 2020; Bio-Ontology Research Group (BORG); Posters; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.