First, we assumed that oncogenic mutations alter the peripheral control of GRN but do not alter the core network topology, where signals processed by a GRN change cell phenotype by engaging a unique gene expression pattern

First, we assumed that oncogenic mutations alter the peripheral control of GRN but do not alter the core network topology, where signals processed by a GRN change cell phenotype by engaging a unique gene expression pattern. metrics for differentiation state aim to inform a more holistic view of how the malignant cell phenotype influences the immune contexture within the tumor microenvironment. score metric Rabbit Polyclonal to MRPL2 may give equal weight to changes in gene expression driven by a biological signal as to changes dominated by random noise. Second, the threshold value provides a rationale for filtering genes that are likely to have a low information content when developing gene signatures Eprodisate Sodium for phenotypes Eprodisate Sodium that are not well defined. Gene Expression Patterns in Breast Malignancy Cells Are Captured by a Single Component Given the variety of breast malignancy subtypes reported in the literature, we next asked how many different GRNs are at work in breast cancer. GRNs associated with development commonly contain transcription factors that interact via positive feedback such that the target genes are either co-expressed or expressed in a mutually unique fashion (Alon, 2007). Given the interest in functional responses, we are focusing on patterns of gene expression in response to signal processing by the GRNs rather than trying to identify their topology. In motivating this study, we made four assumptions. First, we assumed that oncogenic mutations alter the peripheral control of GRN but do not alter the core network topology, where signals processed by a GRN change cell phenotype by engaging a unique gene expression pattern. Second, malignant cells derived from a particular anatomically defined malignancy represent the diverse ways that hijacking these GRNs can provide a fitness advantage to malignant cells within the tumor microenvironment. Third, culturable tumor cell lines represent a sampling of these ways in which GRNs are hijacked in a particular anatomical location. Fourth, the process of isolating these malignant cells from tumor tissue to generate culturable cell lines does not bias this view. It follows then that the number of different GRNs can be identified by analyzing the transcriptional patterns of genes likely to participate in GRNs among an ensemble of tumor cells lines that share a common tissue of origin. We focused our attention on 780 genes that have been previously associated with the EMT and related gene sets in MSigDB v4.0. (Sarrio et?al., 2008, Carretero et?al., 2010, Alonso et?al., 2007, Cheng et?al., 2012, Tan et?al., 2014, Kaiser et?al., 2016, Deng et?al., 2019, Deng et?al., 2020) and analyzed the expression of these genes among 57 breast malignancy cell lines included in the CCLE database as assayed by RNA-seq using a feature extraction/feature selection workflow summarized in Physique?3. To identify coordinately expressed genes, we used principal component analysis (PCA), a linear statistical approach for unsupervised feature extraction and selection that enables the unbiased discovery of clusters of genes that exhibit coherent patterns of expression (i.e., features) that are impartial of other gene clusters (Jolliffe and Cadima, 2016). The relative magnitude of the resulting gene expression patterns can be inferred from the eigenvalues, which represent the extent Eprodisate Sodium of the data’s covariance captured by a specific principal component. To facilitate comparisons among datasets, we represent the eigenvalues as the percent of total sum over all of the eigenvalues or, simply, percent variance, which is usually shown in Physique?4. Specifically, PC1 and PC2 captured 66% and 14% of the variance, respectively. Additional Eprodisate Sodium principal components each captured less than 3% of the variance. Open in a separate window Physique?3 Data Workflow for Identifying Epithelial/Differentiated versus Mesenchymal/De-differentiated State Metrics Workflow contains three decision points: unsupervised feature extraction (FE)/feature selection (FS) based on PCA, a binary fibroblast filter, and a consistency filter based on Ridge logistic regression of annotated samples. Open in a separate window Physique?4 Two Opposing Gene Signatures Were Identified among the Cohort of Breast Malignancy Cell Lines (A) Scree plot of the percentage of variance explained by each principal component, where the dotted line corresponds to variance explained by the null principal components. (B) Projection of the genes along PC1 and PC2 axes, where the font color corresponds to the mean read counts among cell lines (blue-yellow-red corresponds to high-medium-low read.