Molecular subtyping and functional validation of TTK, TPX2, UBE2C, and LRP8 in sensitivity of TNBC to paclitaxel

Abstract

Triple-negative breast cancer (TNBC) patients exhibit variable responses to chemotherapy, suggesting an underlying molecular heterogeneity. In the current study, we analyzed publicly available transcriptome data from 360 TNBC and 88 normal breast tissues, which revealed activation of nucleosome and cell cycle as the hallmarks of TNBC. Mechanistic network analysis identified activation of FOXM1 and ERBB2, and suppression of TP53 and NURP1 networks in TNBC. Employing Iterative Clustering and Guide-gene Selection (ICGS), Uniform Manifold Approximation and Projection (UMAP), and dimensionality reduction analyses, we classified TNBC into seven molecular subtypes, each exhibiting a unique molecular signature, including immune infiltration (CD19, CD8, and macrophages) and mesenchymal signature, which correlated with variable disease outcomes in a larger cohort (1,070) of BC. Mechanistically, depletion of TTK, TPX2, UBE2C, CDCA7, MELK, NFE2L3, DDX39A, and LRP8 led to substantial inhibition of colony formation of TNBC models, which was further enhanced in the presence of paclitaxel. Our data provide novel insights into the molecular heterogeneity of TNBC and identified TTK, TPX2, UBE2C, and LRP8 as main drivers of TNBC tumorigenesis.

Keywords: LRP8; TNBC; TPX2; TTK; UBE2C; bioinformatics; classification; gene signature; heterogeneity; transcriptome.

Graphical abstract

Figure 1

Transcriptional landscape in TNBC compared to adjacent normal tissue (A) Hierarchical clustering of TNBC (n = 200) and adjacent normal tissue (n = 50) based on differentially expressed genes between the two groups. Each column represents one sample, and each row represents a gene. Expression level of each gene (log2) in a single sample is depicted according to the color scale. (B) Principal-component analysis (PCA) for the RNA transcriptome of TNBC (n = 200) and adjacent normal tissue (n = 50). (C) Volcano plot depicting the most differentially expressed genes in TNBC versus normal tissue. (D) Marker finder analysis depicting the list of genes that are selectively expressed in TNBC versus normal breast tissue.

Figure 2

Downstream effector analysis of differentially expressed genes in TNBC and adjacent normal tissue (A) Canonical pathway analysis depicting the most affected canonical pathways in TNBC and normal tissue. Z score correlates with the degree of enrichment. (B) Upstream analysis revealed enrichment and suppression of a number of functional categories in TNBC compared to adjacent normal tissue. (C) Tree map (hierarchical heatmap) depicting affected functional categories based on differentially expressed genes where the major boxes represent a category of diseases and functions. (D and E) Illustration of cellular movement (D) and cell cycle (E) functional categories are illustrated.

Figure 3

Iterative Clustering and Guide-gene Selection (ICGS) analysis revealed heterogeneity in TNBC (A) Cell-type predictions and heatmap performed on TNBC (n = 200) and adjacent normal tissue (n = 50). (B) Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction analysis of TNBC and adjacent normal tissue.

Figure 4

Upstream regulator and functional annotation enrichment in TNBC subtypes Upregulated genes from each TNBC subtype cluster were subjected to comparative upstream and functional annotation analysis using IPA. (A and B) Top affected upstream regulator (A) and functional categories (B) for each TNBC cluster are indicated. Color intensity corresponds to activation Z score. (C) The expression of selected genes from upstream regulator analysis in the indicated clusters. P values were calculated using ANOVA analysis.

Figure 5

Prognostic value of gene signatures from the indicated TNBC molecular subtypes (A and B) Survival heatmap for each TNBC cluster for overall survival (OS) and disease-free survival (DFS). Red color indicated HR >1, while blue color indicates HR <1. Squares with darker edges have the highest prognostic values.

Figure 6

Knockdown of selected genes reduces colony formation potential of TNBCs (A) The expression of TPX2, UBE2C, CDCA7, MELK, NFE2L3, TTK, DDX39A, and LRP8 was validated in a second cohort of 160 TNBC and 38 normal tissue. Transcriptome data were subjected to pseudoallignment using kallisto followed by gene abundance estimation and log2 transformation. Data are presented as dot plot with the corresponding p value indicated. (B) Expression of TPX2, UBE2C, CDCA7, MELK, NFE2L3, TTK, DDX39A, and LRP8 in a number of TNBC cell lines based on cell line encyclopedia database. (C) qRT-PCR for the expression of TPX2, UBE2C, CDCA7, MELK, NFE2L3, TTK, DDX39A, and LRP8 in BT-549, MDA-MB-231, and HCC70 transfected with targeting or scrambled siRNA. GAPDH was used as reference gene. Data are presented as mean ± SD, n = 6. ∗∗∗p < 0.005. (D) Representative CFU for BT-549 cells on day 7 post-knockdown of the indicated genes alone or in combination with paclitaxel (20 nM). Wells are representative of two independent experiments for each treatment condition. (E–G) Quantitative analysis of the effect of gene silencing with and without paclitaxel (20 nM) on the ability of BT-549 (E), MDA-MB-231 (F), and HCC70 (G) CFU is shown. Data is presented as mean ± SD, n = 3.

Figure 7

Cell cycle analysis of TNBC models in response to TPX2, UBE2C, TTK, and LRP8 depletion (A) Histograms illustrate the changes in cell cycle of the indicated TNBC model post-knockdown of TPX2, UBE2C, TTK, or LRP8 as single agent or in combination with paclitaxel (20 nM). (B) Quantification of cell cycle distribution from (A) (n = 3).

Figure 8

Dead-live staining of TNBC models in response to TPX2, UBE2C, TTK, and LRP8 depletion ± PTX Representative fluorescence images for TNBC models post-siRNA-mediated knockdown of TPX2, UBE2C, TTK, and LRP8 alone or in combination with paclitaxel (20 nM). Cells were stained on day 4 with acridine orange/ethidium bromide to detect live (green) and dead cells (red; necrotic).