Additional file 1 of Metformin sensitizes triple-negative breast cancer to histone deacetylase inhibitors by targeting FGFR4

Supplementary Material 1: Fig. S1. Identification of Metformin-Sensitizing Genes via CRISPR-Cas9 Screening. A, The schematic diagram showed how CDTSL identified metformin-sensitizing genes and targeted inhibitors in TNBC. B, The schematic diagram showed the composition of the CDTSL sgRNA sequences. C, The schematic diagram showed the process of CDTSL library screening. D, The Venn diagram showed how 67 candidate genes were identified through MAGeCK analysis to meet the "metformin sensitization" model. E, Functional enrichment analysis revealed a significant enrichment of histone modification-related genes. Fig. S2. The sequencing results of 1462 breast cancer patients (5 cohorts) were displayed. The scatter plot in the upper-left corner compared the expression levels of HDAC10 in tumor tissues versus normal tissues. The remaining subplots analyzed survival differences between patients with high/low HDAC10 expression groups across different cohorts (GSE9893, GSE61304, GSE42568, GSE22219, and TCGA-BRCA) using Kaplan-Meier curves, covering endpoints such as overall survival (OS), disease-free survival (DFS), relapse-free survival (RFS), and progression-free survival (PFS). The p-values from the log-rank test were also annotated. Fig. S3. Combination Efficacy of SAHA and Metformin in TNBC Cell Lines. A, The IC50 curves for SAHA (purple curve) and metformin (orange curve) were shown. The left panel displayed the percentage inhibition (%), while the right panel presented the combination index (CI) at each drug concentration. MDA-MB-231 cells were treated with SAHA, metformin, or both at the indicated concentrations. B, The IC50 curves for SAHA (purple curve) and metformin (orange curve) were shown. The left panel displayed the percentage inhibition (%), while the right panel presented the combination index (CI) at each drug concentration. Hs578T cells were treated with SAHA, metformin, or both at the indicated concentrations. Fig. S4. Colony formation and quantification of MDA-MB-231 cells treated with SAHA, metformin, and combinations. The formed colonies and quantification of MDA-MB-231 cells treated with different concentrations of SAHA (0, 0.125, 0.25, 0.5, or 1 μM), metformin (0, 2 or 4 mM), or their combinations were displayed (in three replicates). Fig. S5. We analyzed differential gene expression, enrichment, and membrane receptor intersections for SAHA and metformin treatments. A, GO-KEGG enrichment analysis of differentially expressed genes (p-value < 0.05 and |Log2FoldChange| > 1) between the SAHA-treated group and the control group. B, Venn diagram showing the intersection of differentially expressed genes between the SAHA-treated group and the control group with membrane receptor genes. C, Heatmap displaying the intersection of differentially expressed genes between the SAHA-treated group and the control group with membrane receptor genes. D, Volcano plot of the differential expression analysis between the SAHA-treated group and the control group (p-value < 0.05 and |Log2FoldChange| > 1). E, GO-KEGG enrichment analysis of differentially expressed genes (p-value < 0.05 and |Log2FoldChange| > 0.8) between the Metformin-treated group and the control group. F, Venn diagram showing the intersection of differentially expressed genes between the Metformin-treated group and the control group with membrane receptor genes. G, Heatmap displaying the intersection of differentially expressed genes between the Metformin-treated group and the control group with membrane receptor genes. H, Volcano plot of the differential expression analysis between the Metformin-treated group and the control group (p-value < 0.05 and |Log2FoldChange| > 0.8). Fig. S6. Protein quantification data from Fig. 3. A, Protein quantification from Fig. 3C showing changes in FGFR4 and STAT3 phosphorylation. MDA-MB-231 cells pretreated with JQ1 for 24 hours were exposed to SAHA for an additional 12 hours. FGFR4 and STAT3 phosphorylation changes were detected by immunoblotting. All bands were quantified from experiments repeated three times. B, Protein quantification from Fig. 3F showing changes in FGFR4 and STAT3 phosphorylation. (Left) MDA-MB-231 cells were treated with the indicated concentrations of SAHA (0, 5, 10 μM) for 12 hours. (Middle) MDA-MB-231 cells were treated with the indicated concentrations of metformin (0, 10, 20, 40 mM) for 48 hours. (Right) MDA-MB-231 cells pretreated with metformin (20 mM) for 36 hours were exposed to SAHA (5 μM) for an additional 12 hours. FGFR4 and STAT3 phosphorylation changes were detected by immunoblotting. All bands were quantified from experiments repeated three times. Fig. S7. We introduced FGFR4 siRNA with SAHA and overexpressed FGFR4 with metformin to observe apoptosis, proliferation, and protein changes. A, Cell apoptosis and growth assays. MDA-MB-231 cells were transfected with non-targeting control (NC) or FGFR4 siRNAs for 48 hours, followed by SAHA treatment for an additional 24 hours for apoptosis analysis, and 72 hours for cell growth analysis. B, Immunoblotting and protein quantification of FGFR4 and MCL-1 change. MDA-MB-231 cells pretreated with non-targeting control (NC) or FGFR4 siRNAs for 48 hours, followed by SAHA (5μM) treatment for an additional 24 hours for Immunoblotting. FGFR4 and MCL-1 change was detected by immunoblotting. All the bands were quantified from experiments repeated three times. C, Cell apoptosis and growth assays. MDA-MB-231 cells were transfected with control or FGFR4 plasmid for 48 hours, followed by metformin treatment for an additional 48 hours for apoptosis analysis, and 72 hours for cell growth analysis. D, Immunoblotting and protein quantification of FGFR4 and MCL-1 change. MDA-MB-231 cells pretreated with control or FGFR4 plasmid for 48h, followed by metformin (20mM) treatment for an additional 48 hours for Immunoblotting. FGFR4 and MCL-1 change was detected by immunoblotting. All the bands were quantified from experiments repeated three times. Fig. S8. Cell apoptosis analysis shown in Fig. S7, performed in triplicates. Fig. S9. Multidimensional analysis of FGFR4 gene necessity, expression profiles, and prognostic impact in pan-cancer and breast cancer. A, DepMap database CRISPR-Cas9 whole-genome screening results: Displaying the top 200 pan-cancer cell lines ranked by FGFR4 CERES scores, reflecting the importance of FGFR4 for cell survival. B, FGFR4 expression levels across various cancer types: Comparison between tumor and normal tissues. C, Association analysis between FGFR4 expression and various clinical features: Including PR status, ER status, HER2 status, PAM50, Pathologic T stage, Pathologic N stage, Pathologic M stage, Pathologic stage, Age, Race, Menopause status, and Histological type. D, Correlation between FGFR4 expression levels and disease-specific survival (DSS) in breast cancer patients (TCGA-BRCA dataset). E, Relationship between FGFR4 expression levels and disease-free survival (DFS) in breast cancer patients (GSE21653 dataset). F, Association between FGFR4 expression levels and relapse-free survival (RFS) in breast cancer patients (GSE9893 dataset). G, Correlation between FGFR4 expression levels and relapse-free survival (RFS) in breast cancer patients (GSE22219 dataset). H-J, Comparison of FGFR4, SLC2A1, and LDHA gene expression between cancer and normal tissues. K, Differential expression of PFKL gene between high and low FGFR4 expression groups. L, Differential expression of SLC2A1 gene between high and low FGFR4 expression groups. Fig. S10. We quantified intratumoral molecular changes using randomly selected visual fields and immunohistochemical metrics. Three visual fields were randomly selected from each group after immunohistochemistry, and the molecular changes shown in Fig. 5 were quantified using two immunohistochemical metrics.