Trung Nghia Vu

Trung Nghia Vu

Principal Researcher
Telephone: +46852482268
Visiting address: Nobels väg 12A, 17165 Solna
Postal address: C8 Medicinsk epidemiologi och biostatistik, C8 MEB I Vu Trung, 171 77 Stockholm
Part of:

About me

  • Personal webpage: https://www.meb.ki.se/sites/truvu/

    - 2009 - 2014: PhD, Department of Mathematics and Computer Science,  
    University of Antwerp, Belgium.
    - 2006 - 2008: MSc, Department of Computer Engineering, Korea Aerospace 
    University, South Korea.
    - 2000 - 2004: BSC, Faculty of Technology, Vietnam National University 
    in Hanoi, Viet Nam.

Research

  • My research focuses on developing and applying statistical and bioinformatics methods for molecular biology and medicine. I am interested in methodologies to analyze single cell data, discover fusion genes, circular RNAs, and driver genes from cancer, quantify isoform and gene expression from transcriptomics and metagenomics, predict drug responses and drug repurposing using pharmacogenomics data.

    Further details can be read at the webpage of my research group: https://ki.se/en/research/research-areas-centres-and-networks/research-groups/trung-nghia-vus-research-group

    Our group has developed 20 statistical and bioinformatics/statistical tools which are mostly published in a peer-reviewed scientific articles and publicly available for academic use. Most of tools are implemented using R, Rshiny, C/C++ and available at the Biostat Wiki  webpage (https://www.meb.ki.se/sites/biostatwiki/)

    Our research is supported by the Swedish Research Council, CancerFonden, and Karolinska Institutet

Teaching

  • SUPERVISION

    To defense

    - Quang Thinh Trac (PhD student, KI, main supervisor, completed Oct 2024)

    - Lu Pan (PhD student, KI, main supervisor, completed Mar 2023)

    - Wenjiang Deng (PhD student, KI, co-supervisor, completed Sep 2021)

    - Thanh Dat Nguyen (master student, Tel-Aviv University, thesis supervisor, completed 2020)

    Ongoing

    - Gulser Caliskan (postdoc, KI, main supervisor, since 2023)

    - Meghana Alugunoolla (master student, Upsala University, main supervisor, since 2024)

    - Yuying Li (PhD student, KI, co-supervisor, since 2021)

    - Aron Arzoomand (PhD student, KI, co-supervisor, since 2021)

    TEACHING

    - Master course “Biostatistics” in the Master of Molecular Techniques, KI, Sweden (2023 & 2024)

    - Doctoral course “Introduction to R for data analysis”, KI, Sweden (2025)

     

Articles

All other publications

Grants

  • Swedish Research Council
    1 January 2024 - 31 December 2026
    The overall aim is to utilize multi-omics approach to identify novel etiopathogenesis and early detection biomarkers for stomach cancer and its precursor lesions. To achieve this aim, first we will use stored serum samples to perform metabolomics profiling among 12,599 twin subjects, among whom 1034 were deemed to have chronic atrophic gastritis based on measured pepsinogen I and II levels. Logistic regression will be used to search for metabolites related to the risk of chronic atrophic gastritis. Second, we will further measure serum proteome by using two quantitatively precise proteomics assays, among the above-mentioned twin subjects. Identified protein biomarkers will be combined with metabolomics biomarkers to create a prediction model for chronic atrophic gastritis. Last, we have created a cohort of subjects who were histopathologically diagnosed with chronic atrophic gastritis or more severe precursor lesions. They were followed for stomach cancer occurrence, and a nested case-control study will be performed. Baseline formalin-fixed paraffin-embedded tissue blocks will be retrieved for both stomach cancer cases and their matched controls, and patterns of tissue proteome and transcriptome will be compared, to identify driving factors associated with progression of precursor lesions to malignancy. The results will hopefully improve our understanding of the etiological factors and provide promising early detection biomarkers for stomach cancer and its precursor lesions.
  • Swedish Cancer Society
    1 January 2023
    Acute myeloid leukemia (AML) is an aggressive blood cancer that is highly genetically complex and cell diversified. The heterogeneity of both tumor genetic changes and cellular subtypes means that response to treatment is very difficult to predict. The response is a crucial part of individualized cancer treatment. While the relationship between drug sensitivity and genetic changes has been largely investigated at the tissue level, studies at the single-cell level, such as cellular heterogeneity, are still limited. We will investigate how response to cancer treatment is affected by the heterogeneity of cells in AML patients. We will evaluate how drug sensitivity is associated with cell heterogeneity in AML tumors detected from single-cell data. We will investigate the diversity of changes by integrating multiple biological features including mutations, immune cell type, RNA expression and alternative splicing patterns. We will use the expanded information, other biologically meaningful features from tissue-level omics data, and advanced machine learning methods to improve prediction of response to individual cancer drugs and drug combinations for patients with AML, and to explore carcinogenic mechanisms of cancer drugs. To develop knowledge about the relationship between drug sensitivity and cell heterogeneity in AML tumors. To develop improved prediction models of drug response for patients with AML.
  • Swedish Research Council
    1 January 2020 - 31 December 2023

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