Journal: iScience

Article Title: Decoding drug-responsive cell subpopulations in triple-negative breast cancer using single-cell multiomics

doi: 10.1016/j.isci.2026.115445

Figure Lengend Snippet: Decoding drug response in patient samples using single-cell identity annotation (A) UMAP plots show cluster identifications, cell types, and treatment conditions for all single cells in the BIOKEY_9 (B9) tumor. These plots provide an overview of the cellular landscape within the tumor, highlighting distinct clusters and their associations with treatment conditions. (B) Single-cell identity changes in the cancer cells of TNBC patient BIOKEY_9 (B9) after pembrolizumab treatment. Pie charts illustrate the distribution of single cells across different identities within the cancer cell population before and after treatment. (C) Identity scores for single cells in the BIOKEY_9 (B9) cancer cell population before and after pembrolizumab treatment. These scores quantify the intensity of gene expression identities at the single-cell level, revealing shifts in cellular identities induced by treatment. Boxplot center line indicates median; box bounds represent the interquartile range (IQR); whiskers extend to 1.5×IQR. (D) GSEA results for F1 and F2 gene sets after pembrolizumab treatment, highlighting the enrichment or depletion of these identity-specific gene sets in the treated cancer cell population. (E) Expression scores for three MsigDB hallmark gene sets in BIOKEY_9 (B9) cancer single cells before and after treatment. The hallmark gene sets analyzed include estrogen response (early and late combined), TNFα signaling via NF-κB, and epithelial-mesenchymal transition (EMT), providing insights into treatment-induced changes in key biological pathways. Violin plots show the distribution of expression scores. (F) Volcano plots of differentially expressed genes (DEGs) in BIOKEY_9 (B9) cancer single cells after pembrolizumab treatment. Pie charts accompanying the plots display the percentage of DEGs associated with each identity in the CCLE model. The right pie chart represents upregulated genes, while the left represents downregulated genes, linking these changes to specific identities. (G) Analysis of identity composition changes in three cancer cell clusters from BIOKEY_9, B9 (cluster_0, cluster_6, and cluster_3), before and after pembrolizumab treatment. Pie charts illustrate the percentage of single cells belonging to each identity within each cluster, revealing differential responses across clusters. (H) Global characterization of identity enrichment or depletion for cancer cells within 11 TNBC tumors following anti-PD1 treatment. Chi-squared test results assess the significance of identity changes across tumors, with stacked bar plots depicting residual scores for each identity. Positive residual scores indicate identity expansion, while negative scores indicate depletion. (I) Analysis of cancer cell clusters contributing to the largest identity expansions in cancer cells for each tumor. Chi-squared test p -values assess whether specific clusters are more associated with identity expansion compared to other clusters within the same tumor. Stacked bar plots represent the residual scores for each cluster, highlighting their contributions to identity changes. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, and ∗∗∗∗ p < 0.0001 by the Wilcoxon rank-sum test (C and E) or by chi-squared test (H and I).

Article Snippet: The right pie chart represents upregulated genes, while the left represents downregulated genes, linking these changes to specific identities. (G) Analysis of identity composition changes in three cancer cell clusters from BIOKEY_9, B9 (cluster_0, cluster_6, and cluster_3), before and after pembrolizumab treatment.

Techniques: Single Cell, Gene Expression, Expressing