Personalized Integrated Network Modeling of the Cancer Proteome Atlas

Min Jin Ha, Sayantan Banerjee, Rehan Akbani, Han Liang, Gordon Mills, Kim Anh Do, Veerabhadran Baladandayuthapani

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general Bayesian framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures. PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches. Towards establishing the translational relevance of the functional proteome in research and clinical settings, we provide an online, publicly available, comprehensive database and visualization repository of our findings (https://mjha.shinyapps.io/PRECISE/).

    Original languageEnglish (US)
    Article number14924
    JournalScientific Reports
    Volume8
    Issue number1
    DOIs
    StatePublished - Dec 1 2018

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    Atlases
    Proteome
    Neoplasms
    Molecular Structure
    Databases
    Precision Medicine
    Proteomics
    Genome

    ASJC Scopus subject areas

    • General

    Cite this

    Ha, M. J., Banerjee, S., Akbani, R., Liang, H., Mills, G., Do, K. A., & Baladandayuthapani, V. (2018). Personalized Integrated Network Modeling of the Cancer Proteome Atlas. Scientific Reports, 8(1), [14924]. https://doi.org/10.1038/s41598-018-32682-x

    Personalized Integrated Network Modeling of the Cancer Proteome Atlas. / Ha, Min Jin; Banerjee, Sayantan; Akbani, Rehan; Liang, Han; Mills, Gordon; Do, Kim Anh; Baladandayuthapani, Veerabhadran.

    In: Scientific Reports, Vol. 8, No. 1, 14924, 01.12.2018.

    Research output: Contribution to journalArticle

    Ha, MJ, Banerjee, S, Akbani, R, Liang, H, Mills, G, Do, KA & Baladandayuthapani, V 2018, 'Personalized Integrated Network Modeling of the Cancer Proteome Atlas', Scientific Reports, vol. 8, no. 1, 14924. https://doi.org/10.1038/s41598-018-32682-x
    Ha, Min Jin ; Banerjee, Sayantan ; Akbani, Rehan ; Liang, Han ; Mills, Gordon ; Do, Kim Anh ; Baladandayuthapani, Veerabhadran. / Personalized Integrated Network Modeling of the Cancer Proteome Atlas. In: Scientific Reports. 2018 ; Vol. 8, No. 1.
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