Additional file 1 of Pan-cancer drivers of metastasis
Creators: name: Lusby, Ryan
name: Demirdizen, Engin
name: Inayatullah, Mohammed
name: Kundu, Paramita
name: Maiques, Oscar
name: Zhang, Ziyi
name: Terp, Mikkel Green
name: Sanz-Moreno, Victoria
name: Tiwari, Vijay K.
Publisher: name: figshare
Year: 2025
Subjects: Medicine
Cell Biology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Developmental Biology
Cancer
Hematology

description: Supplementary Material 1. Supplementary Figure 1. scRNA-seq of each cancer type and scoring of each patient (A) Archetypal analysis of lung and breast cancer patients, with each archetype scored using both gene lists. (B) Gene Ontology (GO) enrichment results for the 286 metastatic gene signature. (C) Stratification of cancer patients based on metastatic scoring. (D) Correlation between the 286-gene signature scores and patient stages (r = 0.134). (E) GO enrichment analysis of the 177-gene signature. (F) GO enrichment analysis of the 109-gene set. (G) Average expression levels of the refined gene signature across TCGA pan-cancer datasets, where * indicates significantly higher expression in tumours compared to normal tissues. Supplementary Figure 2. Spatial transcriptomics scoring and pseudotime analysis (A) Spatial transcriptomic map of a breast cancer patient scored for metastatic potential using a 177-gene signature with UCell. (B) Spatial transcriptomic map of a prostate cancer patient scored for metastatic potential using the same 177-gene signature with UCell. (C) Expression patterns of the metastatic signature across invasive carcinoma and other annotated regions in breast cancer spatial transcriptomic data. (D) Expression patterns of the metastatic signature across invasive carcinoma and other annotated regions in prostate cancer spatial transcriptomic data. Supplementary Figure 3. Genes driving cell type specific metastatic progression (A) Schematic overview of the CellRank method, illustrating how cells are arranged and mapped based on their cellular fate trajectories toward a common endpoint. (B) KEGG pathway enrichment analysis of the top genes driving metastatic progression in epithelial cells. (C) KEGG pathway enrichment analysis of the top genes driving metastatic progression in fibroblast cells. Supplementary Figure 4. Extended pseudotime analysis (A-C) pseudotime analysis with metastatic scoring of cells using UCell. Supplementary Figure 5. Cell-Cell communication analysis. (A) Comparison of signalling pathway–mediated cell–cell communications between high and low metastatic potential samples; (B) comparison of signalling pathway–mediated cell–cell communications between high and medium metastatic potential samples; (C) spatial transcriptomic profiling of a breast cancer patient, illustrating the spatial distribution of gene expression within the tumour microenvironment; (D) identification of top ligands driving cell–cell communications in the breast cancer spatial transcriptomics dataset; and (E) The upper heatmap shows cell-cell interaction between the cell types of high, mid and low metastatic cells. The colour intensity (light to dark) shows stronger interaction between the cell types. The bottom dot plot shows Ligand-receptors involved WNT in cell-cell communication in high, mid and low metatstatic cells. The cell-cell communication analysis was performed using LIANA package and the signalling assessed between epithelial cells and other cell types. F) the venn diagram shows overlap of the downregulated genes upon siSP1 with SP1 bound targets and WNT signaling ligand-receptors of high, mid and low metastatic datasets. Supplementary Figure 6. Cell oracle GRN inference. (A) Overview of the in-silico perturbation approach using CellOracle, illustrating the methodology for modelling gene regulatory networks. (B) Metastatic scoring of breast cancer single-cell RNA-seq (scRNA-seq) data, depicting the transition of cells from low to high metastatic potential. (C) Metastatic scoring of mouse primary tumours and paired liver metastasis scRNA-seq data, highlighting the differences in metastatic potential between primary and metastatic sites. (D) Identification of SP1 as a key regulator in highly metastatic cells and KLF2 as a regulator in medium-scored cells. (E) Pseudotime analysis showing the overlap between metastatic scoring and the transition of cells from low to high metastatic potential. (F) In silico perturbation of SP1, demonstrating its impact on altering the metastatic transition trajectory. Supplementary Figure 7. Gene Ontology Results of SP1 and KLF5 knockdown. (A) Expression levels of the metastatic signature in non-targeting control (siNTC) and SP1 knockdown (KD) MDA-MB-231 (MB231) cells; (B) Expression levels of the metastatic signature in siNTC and KLF5 KD HCC1806 cells; (C) Gene Ontology (GO) enrichment analysis of genes upregulated in MB231 siNTC cells; (D) GO enrichment analysis of genes upregulated in MB231 SP1 KD cells; (E) GO enrichment analysis of genes upregulated in HCC1806 siNTC cells; and (F) GO enrichment analysis of genes upregulated in HCC1806 KLF5 KD cells. (G) Expression of WNT target genes in SP1 KD and siNTC High cell line (H) ASGARD results on SP1 KD and siNTC High cell lines. Supplementary Figure 8. (A) Relative qPCR fold change for the expression of GAPDH, SP1, WNT7B, DVL1, JUNC and NFATC2 upon SP1 overexpression in HCC1806 cells. Each bar indicates the mean of replicate values. Error bar indicates SEM. For statistical analysis, student‘s t-test is performed (* p &lt; 0.05, *** p &lt; 0.001). Gene expression was normalized to the corresponding expression of each gene upon GFP only overexpression in HCC1806 cells. B) Representative images of Immunofluorescent staining for MDA-MB-231-injected mouse breast tumour tissue section. DVL1 in green, WNT7B in red and DAPI in blue. Scale bar indicates 20 microns. C) Representative bright field images of wound healing assay using MDA-MB-231 cells treated with control (siNTC) and SP1 siRNA (siSP1) after 0, 6 and 20 hours. The dashed lines in blue marks the initial wound area at 0h. Scale bar indicates 200 microns. D) Quantification of the relative wound area (%) for the wound healing assay in part A. Each bar represents the mean of triplicate values. Error bars indicate SEM. For statistical analysis, student‘s t-test was performed (* p &lt; 0.05). E). Representative bright field images of wound healing assay using HCC1806 cells transfected with control (GFP-empty) and SP1-overexpressing plasmid (GFP-SP1) after 0, 2, 16 and 24 hours. The dashed lines in blue marks the initial wound area at 0h. Scale bar indicates 200 microns. F) Quantification of the relative wound area (%) for the wound healing assay in part A. Each bar represents the mean of triplicate values. Error bars indicate SEM. For statistical analysis, student‘s t-test was performed (* p&lt; 0.05). G) Quantifications for wound healing assays following various drug treatments in MDAMB231 cells. H) Images of crystal violet-staining for wound healing assay using HCT116 and MDA-MB-231 cells treated with increasing doses of drugs (0, 0.2 uM, 1 uM and 4 uM). C: control (DMSO), V: vorinostat, T: thioridazine, S: salionmycin, N: niclosamide, F: Foxy-5.