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FusionNW: a potential clinical impact assessment of kinases in pan-cancer fusion gene network


bullet pointFusionNW overview
  • Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically important kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none of them used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach so that we can infer how likely individual kinases influence the kinase fusion gene network composed of ~ 5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way of assessment of human kinases in cancer.

    • bullet pointHuman Kinase Fusion Genes
  • Used kinase fusion gene information
    - Human kinase fusion gene information
    : fusion gene name, 5'-gene=kinase (1/0), 3'=kinase=kinase(1/0), fusion gene resource, sample name, 5'-gene, 5'-chromosome, 5'-breakpoint, 3'-gene, 3'-chromosome, 3'-breakpoint
    - Six features of 5K genes involved in kinase fusion genes
    : gene, n_ctypes, n_partners, n_bps, DoF, n_samples, MAII
    - Diverse values to assess individual kinase genes in pan-cancer fusion genes
    : kinase group, kinase gene name, DoF score, # samples, MAII score, and log2(FusionNW output score + 1)

    • - Kinases involved in fusion genes in more than five samples per kinase group


    - Circos plot of the assessment metrics of TK group kinases


    - Circos plots of the assessment metrics of all other kinase groups


    bullet pointAvailable Models and Codes for the FusionNW
  • FusionNW codes
    - PCA feature reduction code
    - Network propagation

  • Requirements
    Software and algorithms to train and run FusionNW
    - python (>3.9)
    - packages: numpy, pandas, sklearn, torch, matplotlib
    • bullet point About us
  • Pora Kim, MS, PhD
  • Email: Pora.Kim@uth.tmc.edu
  • Mailing address:
    •   Center for Computational Systems Medicine
      School of Biomedical Informatics
      The University of Texas Health Science Center at Houston
      7000 Fannin Street, Houston, TX 77030
    bullet point Related citations

        - Kim P*, Tan H, Liu J, Lee H, Jung H, Kumar H, and Zhou X. FusionGDB 2.0: fusion gene annotation update aided by deep learning. Nucleic Acids Res. 2021 Nov 10; doi: 10.1093/nar/gkab1056
        - Kim P*, Tan H, Liu J, Yang M, and Zhou X*, FusionAI: Predicting fusion breakpoint from DNA sequence with deep learning. iScience. 2021 Sep 25; 24(10):103164. doi: 10.1016/j.isci.2021.103164.
        - Kim P*, Yiya K*, and Zhou X*. FGviewer: an online visualization tool for functional features of human fusion genes. Nucleic Acids Res. 2020 Jul 2;48(W1):W313-W320.
        - Kim P and Zhou X*. FusionGDB: fusion gene annotation DataBase. Nucleic Acids Res. 2019 Jan 8;47(D1):D994-D1004