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: 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.

Techniques: Single Cell, Gene Expression, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: High clonal dispersal correlates with increased activity of key processes and signaling pathways in vitro (A) Schematic overview of the experimental pipeline for quantifying clonal dispersal in vitro . Created with BioRender.com . (B) Representative images of the indicated cell lines ( n = 3), transduced with LeGO-NLS constructs. Scale bar: 100 μm, applies to all images. (C) Dispersal scores of indicated cell lines, mean + SD. (D) Pearson’s correlation between the dispersal score and growth rate. (E) Correlation between the dispersal score in CRC cell lines ( n = 13) and expression of human MSigDB hallmark gene sets (Broad Institute). The numbers on the bars indicate the p values. Significance was assessed using unpaired Student’s t tests.

Article Snippet: Scale bar: 500 μm, applies to all images. (C) Quantification of the dispersal score in xenograft sections, mean + SD. (D) Pearson’s correlation between the dispersal score and xenograft growth rate. (E) Correlation between the xenograft dispersal score and expression of human MSigDB hallmark gene sets (Broad Institute).

Techniques: Activity Assay, Protein-Protein interactions, In Vitro, Transduction, Construct, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: Cell-specific dispersal scores are maintained within in vivo xenograft models (A) Schematic overview of the experimental pipeline for quantifying clonal dispersal in vivo . Created with BioRender.com . (B) Representative images of xenografts formed by the indicated LeGO-NLS-transduced cell lines ( n = 3). Scale bar: 500 μm, applies to all images. (C) Quantification of the dispersal score in xenograft sections, mean + SD. (D) Pearson’s correlation between the dispersal score and xenograft growth rate. (E) Correlation between the xenograft dispersal score and expression of human MSigDB hallmark gene sets (Broad Institute). The numbers on the bars indicate the p values. Significance was assessed using unpaired Student’s t tests.

Article Snippet: Scale bar: 500 μm, applies to all images. (C) Quantification of the dispersal score in xenograft sections, mean + SD. (D) Pearson’s correlation between the dispersal score and xenograft growth rate. (E) Correlation between the xenograft dispersal score and expression of human MSigDB hallmark gene sets (Broad Institute).

Techniques: In Vivo, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: A robust dispersal gene signature based on experimentally observed clonal dispersal (A and B) Correlation between gene expression and clonal dispersal of CRC cell lines in vitro (A) and in vivo (B). Significantly positively correlated genes are indicated by the dashed boxes. (C) Venn diagram showing numbers of genes that are positively and significantly correlated with the dispersal scores in either in vitro or in vivo models. Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines. Each dot represents a different CRC cell line, and red dots represent the cell lines used in this study. (F) Dispersal gene signature in CMS1 ( n = 12), CMS2 ( n = 79), CMS3 ( n = 8), and CMS4 ( n = 36) CRC cell lines. Each dot represents a different CRC cell line. Significance was assessed using unpaired Student’s t tests. ∗ p < 0.05.

Article Snippet: Scale bar: 500 μm, applies to all images. (C) Quantification of the dispersal score in xenograft sections, mean + SD. (D) Pearson’s correlation between the dispersal score and xenograft growth rate. (E) Correlation between the xenograft dispersal score and expression of human MSigDB hallmark gene sets (Broad Institute).

Techniques: Gene Expression, In Vitro, In Vivo, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: Dispersal signature correlates with negative clinical outcomes (A) Dispersal gene signature ( Z score) in colon tissue of healthy controls ( n = 72), normal colon tissue of CRC patients ( n = 77), or CRC tissue ( n = 132) ( GSE199057 dataset). (B) Dispersal gene signature ( Z score) in CRC adenoma ( n = 132) and cancer patients ( n = 573) in a microarray meta-dataset. , , , , , , , , , , , (C) Fold change of dispersal signature gene expression between normal and tumor tissue, in different cancer types. (D) Correlation between dispersal gene signature expression and human MSigDB hallmark gene sets in CRC samples ( n = 673, TCGA COAD-READ dataset). (E) Correlation between dispersal gene signature and EMT signature in CRC samples ( n = 673, TCGA COAD-READ dataset). Each dot represents a different tumor sample. (F) Dispersal gene signature expression ( Z score) in CMS1 ( n = 68), CMS2 ( n = 207), CMS3 ( n = 64), and CMS4 ( n = 118) CRCs (TCGA COAD-READ dataset). Each dot represents a different tumor sample. (G) Correlation between copy-number heterogeneity (CNH) and dispersal gene signature ( Z score) in microsatellite stable (MSS) CRC tumors (TCGA COAD-READ dataset). (H) Dispersal gene signature ( Z score) of non-relapsing ( n = 222) and relapsed CRC tumors ( n = 77) ( GSE14333 dataset). (I and J) (I) Overall survival probability and (J) recurrence-free survival probability of CRC patients with either high or low expression of the dispersal gene signature (TCGA COAD-READ dataset). (K) Overall survival probability of CMS4 patients with either high or low dispersal gene signature (TCGA COAD-READ dataset). Significance was assessed using unpaired Student’s t tests for comparisons between two groups, ANOVA followed by a post hoc test for multiple group comparisons and the chi-squared test for survival analysis. Ns, not significant, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, and ∗∗∗∗ p < 0.0001.

Article Snippet: Scale bar: 500 μm, applies to all images. (C) Quantification of the dispersal score in xenograft sections, mean + SD. (D) Pearson’s correlation between the dispersal score and xenograft growth rate. (E) Correlation between the xenograft dispersal score and expression of human MSigDB hallmark gene sets (Broad Institute).

Techniques: Microarray, Gene Expression, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: High clonal dispersal correlates with increased activity of key processes and signaling pathways in vitro (A) Schematic overview of the experimental pipeline for quantifying clonal dispersal in vitro . Created with BioRender.com . (B) Representative images of the indicated cell lines ( n = 3), transduced with LeGO-NLS constructs. Scale bar: 100 μm, applies to all images. (C) Dispersal scores of indicated cell lines, mean + SD. (D) Pearson’s correlation between the dispersal score and growth rate. (E) Correlation between the dispersal score in CRC cell lines ( n = 13) and expression of human MSigDB hallmark gene sets (Broad Institute). The numbers on the bars indicate the p values. Significance was assessed using unpaired Student’s t tests.

Article Snippet: Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines.

Techniques: Activity Assay, Protein-Protein interactions, In Vitro, Transduction, Construct, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: Cell-specific dispersal scores are maintained within in vivo xenograft models (A) Schematic overview of the experimental pipeline for quantifying clonal dispersal in vivo . Created with BioRender.com . (B) Representative images of xenografts formed by the indicated LeGO-NLS-transduced cell lines ( n = 3). Scale bar: 500 μm, applies to all images. (C) Quantification of the dispersal score in xenograft sections, mean + SD. (D) Pearson’s correlation between the dispersal score and xenograft growth rate. (E) Correlation between the xenograft dispersal score and expression of human MSigDB hallmark gene sets (Broad Institute). The numbers on the bars indicate the p values. Significance was assessed using unpaired Student’s t tests.

Article Snippet: Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines.

Techniques: In Vivo, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: A robust dispersal gene signature based on experimentally observed clonal dispersal (A and B) Correlation between gene expression and clonal dispersal of CRC cell lines in vitro (A) and in vivo (B). Significantly positively correlated genes are indicated by the dashed boxes. (C) Venn diagram showing numbers of genes that are positively and significantly correlated with the dispersal scores in either in vitro or in vivo models. Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines. Each dot represents a different CRC cell line, and red dots represent the cell lines used in this study. (F) Dispersal gene signature in CMS1 ( n = 12), CMS2 ( n = 79), CMS3 ( n = 8), and CMS4 ( n = 36) CRC cell lines. Each dot represents a different CRC cell line. Significance was assessed using unpaired Student’s t tests. ∗ p < 0.05.

Article Snippet: Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines.

Techniques: Gene Expression, In Vitro, In Vivo, Expressing

Journal: iScience

Article Title: Clonal dispersal is associated with tumor heterogeneity and poor prognosis in colorectal cancer

doi: 10.1016/j.isci.2025.112403

Figure Lengend Snippet: Dispersal signature correlates with negative clinical outcomes (A) Dispersal gene signature ( Z score) in colon tissue of healthy controls ( n = 72), normal colon tissue of CRC patients ( n = 77), or CRC tissue ( n = 132) ( GSE199057 dataset). (B) Dispersal gene signature ( Z score) in CRC adenoma ( n = 132) and cancer patients ( n = 573) in a microarray meta-dataset. , , , , , , , , , , , (C) Fold change of dispersal signature gene expression between normal and tumor tissue, in different cancer types. (D) Correlation between dispersal gene signature expression and human MSigDB hallmark gene sets in CRC samples ( n = 673, TCGA COAD-READ dataset). (E) Correlation between dispersal gene signature and EMT signature in CRC samples ( n = 673, TCGA COAD-READ dataset). Each dot represents a different tumor sample. (F) Dispersal gene signature expression ( Z score) in CMS1 ( n = 68), CMS2 ( n = 207), CMS3 ( n = 64), and CMS4 ( n = 118) CRCs (TCGA COAD-READ dataset). Each dot represents a different tumor sample. (G) Correlation between copy-number heterogeneity (CNH) and dispersal gene signature ( Z score) in microsatellite stable (MSS) CRC tumors (TCGA COAD-READ dataset). (H) Dispersal gene signature ( Z score) of non-relapsing ( n = 222) and relapsed CRC tumors ( n = 77) ( GSE14333 dataset). (I and J) (I) Overall survival probability and (J) recurrence-free survival probability of CRC patients with either high or low expression of the dispersal gene signature (TCGA COAD-READ dataset). (K) Overall survival probability of CMS4 patients with either high or low dispersal gene signature (TCGA COAD-READ dataset). Significance was assessed using unpaired Student’s t tests for comparisons between two groups, ANOVA followed by a post hoc test for multiple group comparisons and the chi-squared test for survival analysis. Ns, not significant, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, and ∗∗∗∗ p < 0.0001.

Article Snippet: Overlapping genes ( n = 5) were assigned to the dispersal gene signature. (D) Correlation dispersal gene signature expression in CRC lines ( n = 196) and various pathways from the human MSigDB hallmark gene set collection (Broad Institute). (E) Correlation between the dispersal gene signature and EMT signature in 196 CRC cell lines.

Techniques: Microarray, Gene Expression, Expressing