Systematic analysis of tumour cell-extracellular matrix adhesion identifies independent prognostic factors in breast cancer

Abstract

Tumour cell-extracellular matrix (ECM) interactions are fundamental for discrete steps in breast cancer progression. In particular, cancer cell adhesion to ECM proteins present in the microenvironment is critical for accelerating tumour growth and facilitating metastatic spread. To assess the utility of tumour cell-ECM adhesion as a means for discovering prognostic factors in breast cancer survival, here we perform a systematic phenotypic screen and characterise the adhesion properties of a panel of human HER2 amplified breast cancer cell lines across six ECM proteins commonly deregulated in breast cancer. We determine a gene expression signature that defines a subset of cell lines displaying impaired adhesion to laminin. Cells with impaired laminin adhesion showed an enrichment in genes associated with cell motility and molecular pathways linked to cytokine signalling and inflammation. Evaluation of this gene set in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort of 1,964 patients identifies the F12 and STC2 genes as independent prognostic factors for overall survival in breast cancer. Our study demonstrates the potential of in vitro cell adhesion screens as a novel approach for identifying prognostic factors for disease outcome.

Keywords: HER2; breast cancer; cell adhesion; extracellular matrix; laminin.

Figure 1. A subset of breast cancer cell lines display impaired adhesion of laminin

Bar plots showing percentage A. DAPI stained nuclei counts or B. BrdU incorporation in a panel of seven HER2+ breast cancer cell lines on six ECM substrates. Data is normalised to the plastic control (n = 7 or 8). Statistical analysis comparing ECM substrate versus plastic control was performed by paired Student's t test where *p < 0.05, **p < 0.01 and ***p < 0.001. All values are mean ± SD. C. Heatmap of percentage nuclei counts as measured by DAPI staining in the SkBr3 cell line in the presence of pairwise combinations of ECM substrates. Red box highlights combination of laminin with other ECM substrates. D. Bar plots of percentage nuclei counts in SkBr3 cells under conditions of pairwise combinations of laminin and an additional ECM substrate. Data is normalised to uncoated plastic control (n = 3). Statistical analysis of combined ECM substrate versus laminin only was performed by paired Student's t test where *p < 0.05 and **p < 0.01. All values are mean ± SD. E. Heatmap of percentage BrdU incorporation in the SkBr3 cell line in the presence of pairwise combinations of ECM substrates. Red box highlights combination of laminin with other ECM substrates. F. Bar plots of percentage BrdU incorporation in SkBr3 cells under conditions of pairwise combinations of laminin and an additional ECM substrate. Data is normalised to uncoated plastic control (n = 3). All values are mean ± SD.

Figure 2. Impaired laminin adhesion cell lines do not cluster based on expression of specific ECM genes or kinases

Hierarchical clustering of normalised microarray gene expression data for A. KEGG annotated ECM pathway genes and B. kinases. Each row was normalised before clustering to give a mean of 0 and a standard deviation of 1. Red indicated higher expression of a gene in the cell line and blue indicates lower expression. Cell lines with impaired laminin adhesion are labelled in red.

Figure 3. Genes that are correlated with impaired laminin adhesion

A. Gene expression heatmap for the top 50 genes positively and negatively correlated with the impaired laminin adhesion cell lines. Gene markers were identified using GSEA. Each row of the heatmap was normalized to give a mean of 0 and a standard deviation of 1. Red indicates higher expression of a gene and blue indicates lower expression. FDR values for all 100 genes were calculated with p-values shown to be < 0.05. B. Normalized enrichment scores for GSEA analysis of MSigDB curated pathways / gene sets enriched in the impaired laminin adhesion cell lines with nominal p-values < 0.05. Bars highlighted in red are those pathways associated with cytokine signalling and inflammation.

Figure 4. Association between F12 and STC2 gene expression and disease-specific survival in breast cancer

Kaplan-Meier curves illustrate differences in disease-specific survival in patient groups in two subsets stratified by A. F12, B. STC2 and C. F12 + STC2 gene expression levels. The thresholds for dichotomising two indices were optimised in the discovery cohort (left) and then used without modification in the validation cohort (right) (0.2718 for F12, 0.2801 for STC2). Numbers in the legend show the number of patients in each group and numbers in brackets show the number of disease-specific deaths. Log-rank test p-values show significant differences between F12-high/STC2-low and F12-low/STC2-low groups. NS is not significant.