A combinatorial extracellular matrix platform identifies cell-extracellular matrix interactions that correlate with metastasis

Abstract

Extracellular matrix interactions have essential roles in normal physiology and many pathological processes. Although the importance of extracellular matrix interactions in metastasis is well documented, systematic approaches to identify their roles in distinct stages of tumorigenesis have not been described. Here we report a novel-screening platform capable of measuring phenotypic responses to combinations of extracellular matrix molecules. Using a genetic mouse model of lung adenocarcinoma, we measure the extracellular matrix-dependent adhesion of tumour-derived cells. Hierarchical clustering of the adhesion profiles differentiates metastatic cell lines from primary tumour lines. Furthermore, we uncovered that metastatic cells selectively associate with fibronectin when in combination with galectin-3, galectin-8 or laminin. We show that these molecules correlate with human disease and that their interactions are mediated in part by α3β1 integrin. Thus, our platform allowed us to interrogate interactions between metastatic cells and their microenvironments, and identified extracellular matrix and integrin interactions that could serve as therapeutic targets.

Figure 1. Extracellular Matrix Microarray Platform Presents Combinations of ECM Molecules for Cell Attachment

(a) ECM microarrays are generated by spotting nearly 800 unique combinations of ECM molecules on glass slides coated with polyacrylamide followed by seeding of cells onto the slides. (b) Polyacrylamide acts to entrap molecules of a large range of molecular weights. (c) Verification of presentation of all molecules by immunolabeling (colored spots) or NHS-fluorescein labeling (grayscale spots) of all molecules subsequent to array generation and rehydration. (d) Representative images of cells adhered to ECM spots demonstrating selective adhesion in the locations of ECM. Scale bar on five-spot image is 200µm. Scale bars on single-spot images are 50µm.

Figure 2. Combinatorial Adhesion Profiles are generated using ECM microarrays

(a) Nuclear stain of cells seeded on the ECM microarrays. (b) Identification of individual nuclei on one spot using CellProfiler. (c) Quantification of adhesion to all molecule combinations for one cell line. (d) Selected adhesion profiles for three molecules: Collagen I (blue), Collagen IV (green), and Fibronectin (red) in combination with all other molecules. Dashed blue lines represent adhesion to that molecule alone. Arrows denote combinations with the other two molecules or alone. Error bars are s.e.m. of three replicate slides. (e) Comparisons of three replicate slides for two representative cell lines. Scale bars in (a) and (b) are 450µm and 100µm, respectively.

Figure 3. ECM Microarrays identify key adhesive changes in metastatic progression

(a) Unsupervised hierarchical clustering of adhesion profiles generated by the ECM microarrays. Vertical axis represents different ECM combinations. Horizontal axis represents different cell lines. Yellow bars indicate primary tumors (T_nonMet and T_Met lines). Red bars indicate nodal (N) or distant metastases (M). (b) Average adhesion of metastatic cell lines (M) to each combination compared to those of the metastatic primary tumor cell lines (T_Met). (c) Comparison of 393M1 adhesion for each combination to its matching primary tumor line, 393T5. Red dots indicate top ECM combinations exhibiting preferential adhesion by metastatic lines over the metastatic primary tumor lines. (d) Top three combinations exhibiting the greatest increase in adhesion across tumor progression as represented by the four classes of cell lines (T_nonMet, T_Met, N, and M). Error bars in (d) are s.e.m. of the different cell lines of each class (n = 3 cell lines per class) with the exception of the M class where there are two lines, and thus the error bars are the range of the means.

Figure 4. Metastasis-associated ECM molecules are present in the sites of metastases but not primary tumors

Immunostaining of the metastasis-associated ECM molecules in the lungs, lymph nodes, and distant metastases of mice bearing endogenous lung adenocarcinomas (Kras^LSL-G12D/+;p53^flox/flox mice). Insets are magnified views of boxed areas showing ECM molecule fibrils. Number of tissues examined for each organ: Lungs: 10; lymph nodes: 5; livers/kidneys: 22. ‘T’: tumor. Dotted line depicts edge of tumor and normal kidney. Scale bars are 50µm.

Figure 5. ECM production and integrin mRNA expression by cell lines have minimal correlation with adhesion

(a) Western blot analysis of the metastasis- and primary tumor-associated ECM molecules produced by the 393T5 (T_Met) and 393M1 (M) cell lines. (b) Comparison of ECM adhesion for all cell lines to gene expression of the cognate integrins from gene expression microarray data. (c) Integrin subunit mRNA expression from Affymetrix microarray analysis in 393T5 and 393M1 cell lines.

Figure 6. Integrin Surface Expression Correlates with ECM binding profiles

(a) Flow cytometry of integrin surface expression in 393T5 (T_Met) and 393M1 (M) cell lines. Integrin subunits that bind to metastasis-associated molecules show increased surface presentation in the metastatic line (α5, αv, α6, α3), while those that bind to primary tumor-associated molecules show decreased presentation (α1 and α2). (b) IHC for metastasis-associated integrins in mice bearing autochthonous tumors with spontaneous metastases to the liver and lymph nodes. Scale bars are 100µm.

Figure 7. Integrin α3β1 mediates adhesion and seeding in vitro and in vivo

(a) in silico network mapping using GeneGO (MetaCore) generates the Lung Adenocarcinoma Metastasis Network. Analysis of the network reveals that integrin a3b1 is the surface receptor with the most edges (a). Knockdown of both α3 and β1 integrin subunits by shRNA reduces adhesion to metastasis-associated molecules in vitro (b) and prevents metastatic seeding in vivo (c-e). shFF is the control hairpin targeting firefly luciferase. One-way ANOVA with Tukey’s Multiple Comparison Test was used to analyze the data in figure (b). Error bars in (b) represent standard error (n = 3). (c) Number of liver tumor nodules of the surface of livers 2.5 weeks after intrasplenic injection. Mann-Whitney (non-parametric) test was used to analyze significance. (d) Fluorescence imaging of whole livers after resection. Cell lines express nuclear-excluded ZSGreen. Scale bars are 0.5cm. (f) Hematoxylin and eosin stain of liver slices. Scale bars are 2mm. Blue data points in (d) correspond to images in (e) and (f). All results shown are representative of multiple independent experiments.

Figure 8. Metastasis-associated molecules are present in the metastases of human lung cancers

(a-d) Oncomine results for human lung cancer expression of LGALS3 and LGALS8. (a) LGALS3 Expression in Hou Lung: Large Cell Lung Carcinoma – Advanced Stage. (b) LGALS3 Expression in Bild Lung: Lung Adenocarcinoma – Advanced Stage. (c) LGALS8 Expression in Hou Lung: Large Cell Lung Carcinoma – Advanced Stage. (d) LGALS8 Copy Number in TCGA Lung 2: Lung Adenocarcinoma – Advanced M Stage. LGALS3 and LGALS8 are overexpressed in Stage II lung cancer compared to stage I (P = 0.018 and 9.72E-4, respectively)(a,c). Microarray data source GSE19188. (b) LGALS3 is overexpressed in Stage IV lung cancer compared to other stages (P = 0.040). Microarray data source GSE3141. (d) LGALS8 has increased copy number in advanced M stage lung cancer (P = 0.013) in the “Lung Carcinoma DNA Copy Number Data” dataset available from The Cancer Genome Atlas website (https://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp). (e) Representative images of human tissue microarray staining results for galectin-3 presence or absence in the primary sites and lymph nodes. Scale bars are 500µm. Box and whisker plots in (a-d): dots represent maximum and minimum values, whiskers show 90^th and 10^th percentiles, boxes show 75^th and 25 percentiles, and line shows median. P-values in (a-d) were computed by Oncomine software using Student’s t-test (a, c-d) or Pearson’s correlation analysis (b).