A scatterplot displaying gene expression levels against tumor purity is first displayed. The “Gene” module allows users to input a gene symbol, cancer types, and immune cell types of interest. Malignant cells and TIICs interact through multiple gene products and pathways during tumor development and progression. In brief, we select informative genes which are negatively correlated with tumor purity (percentage of malignant cells in a tumor tissue) for each cancer type ( 10) and apply constrained least squares fitting on the expression of selected genes to predict the abundance of six TIIC subsets. The workflow is based on our previous work of statistical deconvolution of immune infiltrates ( 9). In this work, we re-analyze gene expression data, which includes 10,897 samples across 32 cancer types from The Cancer Genome Atlas (TCGA) to estimate the abundance of six TIIC subsets (B cells, CD4 T cells, CD8 T cells, macrophages, neutrophils, and dendritic cells). TIMER applies a deconvolution method we previously published ( 9) to infer the abundance of TIICs from gene expression profiles. Therefore, we developed an interactive web application, TIMER, as a public resource to enable cancer biologists to analyze and visualize the abundance of TIICs in a comprehensive and flexible manner. In addition, PRECOG and TCIA have limited analysis and visualization functions.ĭespite these efforts, a comprehensive computational tool for cancer researchers to conveniently explore and visualize tumor immunological and genomics data is still lacking. However, CIBERSORT estimations are potentially affected by statistical multicollinearity due to the inclusion of highly correlated immune cell types, leading to high estimations of uncertainty ( Supplementary Material). Its results have been presented as online resources, such as PRECOG ( 6) and TCIA ( 7), to analyze the impact of TIICs and individual gene expression levels on clinical outcome. One influential deconvolution method, CIBERSORT, utilizes microarray data with a pre-defined immune signature matrix to estimate the fraction of 22 tumor-infiltrating immune cells (TIICs) within a given sample ( 5). Immune signature genes have also been used to characterize immune infiltrates and predict clinical outcome ( 4). One seminal study measures effector cell cytolytic activity using the transcript levels of two genes, GZMA and PRF1, to elucidate possible mechanisms of immune evasion ( 8). Recently, a variety of computational methods to infer immune infiltrates have been developed to investigate tumor immunology and develop new effective immunotherapies ( 4– 7). Due to the complexity of tumor genomes and the plasticity of the host immune system, it remains challenging to characterize the interaction between cancer cells and immune infiltrates ( 3). Immune infiltrates obtained from different patients with the same tumor type are heterogeneous and may impact clinical outcome ( 2). Recent advances in immunotherapy, especially checkpoint blockade, have resulted in clinical success in treating late-stage cancers ( 1).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |