Decomposition of mixed transcriptomes for classification of heterogeneous tumour samples

    Project Details


    The majority of samples studied by massive parallel techniques such as microarrays or next-generation sequencing
    are heterogeneous at the cellular level. This is especially true for tumours, where different subpopulations of cancer
    cells are mixed with stromal cells. Bulk tissue analysis using the conventional approach averages cellular
    expression data, which can lead to observation of an unrealistic combination of expressed transcripts and hide the
    expression of lowly expressed, but important genes. These facts limit research discoveries, mask the biological
    processes and may lead to clinical issues during diagnosis of cancer patients. The main goal of the project is to
    improve patient classification using information about statistically independent transcriptional signals found in an
    ensemble of cell subpopulations. To deconvolute the bulk transcriptome into such signals, the independent
    component analysis method will be used for gene expression and exon junction expression data. By considering
    junctions, we will account for variability at the gene isoform level and more specifically, target the distinct cell
    subtypes. Thus, independent component analysis will serve as a feature selection method for the following
    classification of the samples. Several classifiers will be considered in the project; the best performing one will be
    trained on publicly available data and validated on an independent dataset. Brain gliomas of different stages and
    two non-small-cell lung cancers were chosen as prototypical examples of heterogeneous cancers. By in silico
    deconvolution of the bulk samples we will not only improve the sensitivity of classifiers to lowly abundant cell
    populations, but also provide new biological knowledge about processes taking place in the distinct tumour and
    stroma cell subtypes. We will identify key regulators responsible for these processes – this information can later be
    used to define proper therapeutic targets.
    Effective start/end date1/01/1831/01/21


    • FNR - Fonds National de la Recherche: €280,000.00


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