|A combinatorial extracellular matrix platform identifies cell-extracellular matrix interactions that correlate with metastasis.|
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|PMID: 23047680 Owner: NLM Status: MEDLINE|
|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.|
|Nathan E Reticker-Flynn; David F Braga Malta; Monte M Winslow; John M Lamar; Mary J Xu; Gregory H Underhill; Richard O Hynes; Tyler E Jacks; Sangeeta N Bhatia|
|Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't|
|Title: Nature communications Volume: 3 ISSN: 2041-1723 ISO Abbreviation: Nat Commun Publication Date: 2012|
|Created Date: 2012-10-10 Completed Date: 2013-02-01 Revised Date: 2014-06-03|
Medline Journal Info:
|Nlm Unique ID: 101528555 Medline TA: Nat Commun Country: England|
|Languages: eng Pagination: 1122 Citation Subset: IM|
|APA/MLA Format Download EndNote Download BibTex|
Cell Line, Tumor
Extracellular Matrix / metabolism*
Fibronectins / metabolism
Galectin 3 / metabolism
Galectins / metabolism
Laminin / metabolism
Lung Neoplasms / metabolism*, pathology
|K99 CA151968/CA/NCI NIH HHS; P30 CA014051/CA/NCI NIH HHS; R00 CA151968/CA/NCI NIH HHS; U54 CA112967/CA/NCI NIH HHS; U54 CA126515/CA/NCI NIH HHS; U54 CA163109/CA/NCI NIH HHS; U54CA112967/CA/NCI NIH HHS; U54CA126515/CA/NCI NIH HHS; //Howard Hughes Medical Institute; //Howard Hughes Medical Institute|
|0/Fibronectins; 0/Galectin 3; 0/Galectins; 0/Laminin; 0/galectin-8, mouse|
Journal ID (nlm-journal-id): 101528555
Journal ID (pubmed-jr-id): 37539
Journal ID (nlm-ta): Nat Commun
Journal ID (iso-abbrev): Nat Commun
nihms-submitted publication date: Day: 19 Month: 5 Year: 2013
Print publication date: Year: 2012
pmc-release publication date: Day: 10 Month: 10 Year: 2013
Volume: 3First Page: 1122 Last Page: 1122
PubMed Id: 23047680
|A combinatorial extracellular matrix platform identifies cell-extracellular matrix interactions that correlate with metastasis|
|Nathan E. Reticker-Flynn12|
|David F. Braga Malta123|
|Monte M. Winslow14|
|John M. Lamar1|
|Mary J. Xu2|
|Gregory H. Underhill15|
|Richard O. Hynes167|
|Tyler E. Jacks1678|
|Sangeeta N. Bhatia127910*|
1David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
2Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
4Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
5Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
6Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
7Howard Hughes Medical Institute, Cambridge, MA 02139, USA
8Ludwig Center for Molecular Oncology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
9Division of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA
10Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
|*To whom correspondence should be sent: email@example.com
Cancer metastasis is a poorly understood multistep process that results in 90% of cancer-related deaths1–2. At the time of initial diagnosis, almost half of lung adenocarcinoma patients have detectable metastases and the majority of the remaining half will relapse with metastatic disease after surgical removal of the primary tumor and adjuvant chemotherapy3. Despite the ominous nature of metastatic disease, the molecular mechanisms that drive each step are poorly characterized and few effective therapies exist4. Recently, it has become apparent that the tumor microenvironment dramatically impacts metastatic progression5. Changes in cancer cell-extracellular matrix (ECM) interactions likely influence each stage of the metastatic cascade, starting with the loss of basement membrane adhesion to colonization of distant sites. Furthermore, alterations in matrix production and crosslinking can promote metastasis6–8. Consequently, inhibiting interactions of tumor cells with their microenvironments by targeting adhesion molecules is an area of active investigation9–10.
While a variety of techniques exist for studying microenvironmental interactions, it has been challenging to date to interrogate the functional implications of specific cell-ECM interactions in a high-throughput manner. Injection of metastatic cells into embryos documented the anti-tumor effects of the embryonic microenvironment11–12, and coculture studies have identified the roles of carcinoma-associated fibroblasts on tumor progression13. ECM-coated transwells have been used to study the effects of small numbers of individual candidate ECM molecules on 2D invasion14, and 3D collagen gels have been useful particularly in the study of matrix metalloproteinase activity15. In vivo studies using gene-targeted mice have documented the importance of several ECM molecules and their receptors in transplant-based models of cancer and metastasis16–17. Each of these techniques has documented key microenvironmental regulators of metastasis, but they have not allowed an unbiased systematic evaluation of the role that ECM components play.
Cell-ECM interactions are particularly difficult to study due to their complexity of synergistic and antagonistic interactions in vivo18. Experiments targeting integrins, a central family of cell surface receptors that mediate ECM interactions, have implicated integrin-ECM interactions as important regulators of cancer progression9,19–20. However, in addition to adhesion, integrins regulate stress transmission and bidirectional signaling, and typically bind multiple ECM molecules21. Furthermore, transmembrane collagens, syndecans, lectins, carbohydrates, gangliosides, glycolipids, CD44, and dystroglycans are among a host of non-integrin ECM receptors. Thus, techniques that allow the specific unbiased interrogation of cell-ECM adhesion are required to directly query the diversity of potential interactions.
In this study, we describe a high-throughput platform capable of systematically uncovering cell-ECM interactions, and use this method to characterize the global changes in ECM adhesion in a model of cancer progression. We previously described a first-generation platform that utilized robotic spotting technology to generate arrays with combinations of five ECM molecules found in normal basement membrane and connective tissue22. Since then, others have utilized similar platforms to investigate ECM responses23–26. While these platforms have demonstrated feasibility of such approaches in physiologic processes such as differentiation of stem cells, they have not yet been applied to increase our understanding of disease states. Furthermore, their limited size (typically five different ECM molecules) has prevented them from querying the diversity of ECM interactions present in the human body. Here, we present an expanded ECM microarray platform containing 768 unique pairwise ECM molecule combinations expressed differentially in development, regeneration, and disease including an expanded representation of proteoglycans and glycosaminoglycans which are difficult to study through integrin manipulation alone, and apply them to investigate changes in adhesion throughout metastatic progression. We have established a highthroughput pipeline to generate these microarrays that utilizes liquid handlers for mixing of source ECM, optimized cell-seeding devices, and automated image capture and analysis. We studied the adhesion profiles of lung adenocarcinoma cell lines generated from a genetically engineered mouse model where discrete stages of metastatic progression have been defined, and correlated the findings with in vivo ECM distributions in mice and humans with metastatic lung cancer27–29. This approach is easily extensible to other disease states, ECM combinations, and phenotypic readouts.
To allow the unbiased study of the ECM adhesion characteristics of any cells-of- interest, we developed a novel high-throughput platform. We expanded, automated, and optimized our adhesion platform22–23 to include every single and pairwise combination of 38 unique ECM molecules (Supplementary Table S1). Thus, these arrays contain 768 different combinations in quintuplicate and 160 control spots, for a total of 4000 arrayed features. To fabricate the arrays, the 38 ECM molecules and controls are transferred from a 96-well source plate to two low-volume 384-well plates and mixed thoroughly using a robotic liquid handler. These 384-well plates are then used as source plates for deposition of the matrix combinations onto the slides by a DNA microarray spotter. Prior to deposition of the molecules, slides are coated with a polyacrylamide hydrogel that is allowed to dry after soaking to remove any unpolymerized monomer. The dehydrated hydrogel acts to entrap molecules without requiring their chemical modification (Fig. 1a). Our data indicate that molecules larger than ∼10kDa can be robustly entrapped in the hydrogel (Fig. 1b), and we verified their entrapment using NHS-Fluorescein labeling or antibody-mediated detection after entrapment (Fig. 1c). Of the 38 molecules that we tested by these methods, all showed excellent reproducibility and uniformity within the expected region of printing (Fig. 1c).
To measure cell-ECM interactions, cells are seeded onto the arrays in serum-free media and allowed to adhere for 1.5 hours at 37°C (Supplementary Fig. S1a). To ensure uniform seeding, the slides are agitated every fifteen minutes. Furthermore, the top surfaces of the slides are held flush with the bottom of the plate through the use of a custom-designed seeding device that employs a vacuum seal (Supplementary Fig. S1b and 1c). This device minimizes seeding variability between experiments and avoids cell loss by preventing cells from settling below the slide surface or on the backs of the slides. Uniformity of seeding across individual arrays and between replicate arrays was confirmed using test slides composed of only one matrix molecule. To quantify cells bound to each spot, nuclei are stained according to conventional fluorescence staining protocols, and the slides are imaged using an automated inverted epifluorescent microscope with NIS Elements software (Fig. 1d and 2a). Large images are cropped to individual spots and indexed using MATLAB (Mathworks), and adhesion is quantified using CellProfiler software to detect and count nuclei (Fig. 2b)30. Subsequent image and data analysis is performed in MATLAB.
To uncover changes in the global adhesion profile of cancer cells during cancer progression and metastatic spread, we analyzed a panel of murine lung adenocarcinoma cell lines derived from non-metastatic primary tumors (TnonMet), primary tumors that metastasized (TMet), or lymph node (N) and liver (M) metastases (Supplementary Table S2)29. These cell lines were derived from a genetically-engineered mouse model of metastatic lung adenocarcinoma in which tumors were initiated in KrasLSL-G12D/+;p53flox/flox mice with lentiviral-Cre vectors. The stable and random integration of the lentiviral vector allowed the clonal relationship between the multifocal primary tumors and metastases to be established29. Analysis of the adhesion profiles of these cell lines highlighted the diverse adhesion of each line to different ECM combinations (Fig. 2c). Our analysis of these cell lines revealed highly reproducible adhesion between replicate spots and arrays, confirming the quantitative nature of the assay (Fig. 2d). We examined the profiles to interrogate whether various populations exhibit enhanced adhesion to combinations of ECM molecules, relative to the same molecules spotted in isolation. This analysis revealed that different pairwisecombinations of ECM molecules result in additive, synergistic, and antagonistic effects on adhesion. For example, for the TnonMet cell line shown in Figures 2c and 2e, many molecules improve adhesion to collagen I, while others reduce cell binding in comparison to the molecule in isolation (blue line, Fig. 2e). A similar range of responses was observed for other molecules, including collagen IV and fibronectin (Figure 2e). These types of combinatorial effects were present for many molecules and, while the specific patterns varied, all cell lines tested exhibited examples of increased and reduced binding to various ECM combinations. For each of the eleven murine lung adenocarcinoma cell lines tested, distinct profiles that were highly reproducible across replicate slides were obtained (Fig. 2d).
We used the ECM microarrays to compare the adhesion profiles of populations from each of the TnonMet, TMet, N, and M classes of cell lines. We applied unsupervised hierarchical clustering analysis of the adhesion values in a manner analogous to clustering of gene expression microarray data. Interestingly, all the cell lines derived from metastases (N or M), save for one lymph node line, clustered independently from the cell lines derived from primary lung tumors (TnonMet or TMet) (Fig. 3a). This result is particularly notable, since two of the metastatic lines (393M1 and 389N1) were generated from metastases that originated from two of the primary tumors (393T5 and 389T2, respectively), yet clustered more closely to the other metastases than the lines derived from those primary tumors. Thus, there is a conserved change in the ECM adhesion profile of cancer cells present in a metastatic site versus those that remain in the primary tumor. Furthermore, this differential clustering was not evident from unsupervised hierarchical clustering of the gene expression of these lines (ref29 and Supplementary Fig. S2), suggesting that the metastasis-specific adhesion phenotype provides a complementary, non-overlapping view of the molecular mediators that influence metastatic progression.
In light of the hierarchical clustering results, we asked whether there were particular combinations of molecules that are favored by metastatic cells rather than by cells from primary tumors. Thus, we compared the average adhesion of the liver metastasis-derived cell lines (M) for each ECM combination to the average adhesion of the TMet lines (Fig. 3b). While many of the M lines exhibit elevated binding to combinations containing fibronectin, pairings that combined fibronectin with any of galectin-3, galectin-8, or laminin had the highest differential adhesion between line classes. To explore changes in adhesion that specifically correlated with changes in metastatic progression, we compared the TMet cell line 393T5 and the clonally-related liver metastasis-derived cell line 393M1. This pair of lines was derived from a primary tumor and a metastasis that disseminated from that tumor, as confirmed by examination of the lentiviral integration site 29. Furthermore, the differential adhesion to the aforementioned ECM combinations was clear in both the group-wise comparison (Fig. 3b) and in the direct comparison of this primary tumor-liver metastasis pair (Fig. 3c).
Collectively, the patterns observed suggest that combinations of molecules may play a more significant role in the adhesion profile of a given population than the tendency to bind to any of the ECM molecules alone. Interestingly, the trend towards increased binding to fibronectin/galectin-3, fibronectin/laminin, and fibronectin/galectin-8 combinations was consistent across tumor progression when we compared the average adhesion of all TnonMet, TMet, N, and M cell lines (Fig. 3d). Binding to these molecules, when presented alone, showed minimal (fibronectin) or no trend (laminin, galectin-3, and galectin-8) across the four groups of cell lines (Supplementary Fig. S3a, red bars). When in combination, however, these pairs demonstrate enhanced effects that exceed the additive values of their individual adhesion. In contrast, other combinations demonstrated a reduced adhesion trend in relatively more metastatic populations, including a variety of collagens and osteopontin (Supplementary Fig. S3a–c). Taken together, these data suggest that adhesion to fibronectin in combination with any of galectin-3, galectin-8, or laminin is highly associated with tumor progression in this model system.
We also noted that some combinations of molecules appear to elicit antagonistic effects on adhesion. We looked more closely at the adhesion profile of the M line 393M1 (Supplementary Fig. S4a) and observed that, while the metastasis-associated molecules laminin, galectin-3, and galectin-8 all increase adhesion to fibronectin, other molecules appeared to decrease adhesion to it (Supplementary Fig. S4b). In vitro adhesion assays using co-adsorbed ECM to multiwell polystyrene plates confirmed that the addition of these molecules does indeed decrease the adhesion of this line to fibronectin (Supplementary Fig. S4c). Collectively, the existence of both synergistic and antagonistic effects highlights the importance of investigating combinations of ECM molecules rather than isolated components.
Next we sought to correlate our in vitro adhesion profiles with ECM expression in vivo. To investigate whether the identified ECM molecules may be important in natural tumorigenesis, organs containing primary autochthonous tumors and their metastases were resected from KrasLSL-G12D/+;p53flox/flox mice and stained. Trichrome staining of lungs with extensive tumor burden revealed a significant presence of ECM deposition in the tumor-bearing lung (Supplementary Fig. S5a and ref 31). Previously, we found that primary tumors that have acquired the ability to metastasize (TMet tumors) upregulate the chomatin-associated protein Hmga229. Therefore, we used Hmga2 immunohistochemistry in addition to histological characteristics to identify areas of highly aggressive cancer cells (Supplementary Fig. S5a). As anticipated, primary lung tumors were positive for collagen I (black arrowheads), collagen VI (open black arrowheads), and osteopontin (red arrowheads), with the most intense staining overlapping with the high-grade tumor areas (Supplementary Fig. S5a and S5c). In particular, osteopontin staining strongly co-localized with Hmga2pos regions, suggesting that increased osteopontin production is associated with metastatic primary lung tumors. Furthermore, little to no laminin, galectin-3, or galectin-8 staining was detected in the primary tumors (Fig. 4). Interestingly, fibronectin staining in the tumor was strong, revealing a correlation between increasingly metastatic populations and the presence of fibronectin early in the metastatic cascade (Fig. 4).
We next asked whether the lymph node and distant organ metastases contained the metastasis-associated ECM molecules. Again, trichrome staining revealed the presence of significant matrix deposition within the lymph nodes (Supplementary Fig. S5b). As expected, the entirety of the lymph node tumors were histologically high-grade and were Hmga2pos (Supplementary Fig. S5b). There was also clear expression of all four of the metastasis-associated molecules (fibronectin, laminin, galectin-3, and galectin-8) within the lymph node metastases (Fig. 4). Furthermore, there was essentially no collagen I or collagen VI (Supplementary Fig. S5c). Interestingly, osteopontin was present in the metastases (Supplementary Fig. S5c) and had its highest expression along the invasive front (Supplementary Fig. S5a,d).
We also examined common metastatic sites for the presence of the metastasis-associated molecules (Fig. 4). Both galectin-3 and galectin-8 were distinctly visible in these sites. Laminin and fibronectin both appeared to line the sinusoids of the livers of the mice and were also present in the metastases formed there. To determine whether these differences between the primary and metastatic sites were due to altered matrix production by the tumor cells, we performed immunoblots on the 393T5 and 393M1 TMet and M cell lines. While the M line showed slight increases in fibronectin and laminin production compared to the TMet line, production of both galectins was constant (Fig. 5a). Furthermore, collagen I production was constant, and osteopontin production was actually increased in the M line. Taken together, these data suggest that the ECM microarrays identified molecules that were found within the physiologically relevant sites of mice bearing autochthonous tumors, and that production of these molecules is not solely performed by the tumor cells present in those sites.
We noted that comparisons of adhesion trends on our ECM arrays did not necessarily correlate with transcriptional profiles of the cognate integrins (Fig. 5b). Thus, to correlate our findings with the presence of receptors for these metastasis-associated ECM molecules, we examined the clonally-related pair of representative TMet and M cell lines for surface expression of their cognate integrins. While the mRNA expression patterns did not show significant upregulation of the metastasis-associated integrins in the M line by gene expression microarray (Fig. 5c), flow cytometry analysis of the integrin subunits corresponding with either the primary tumor-associated molecules or metastasis-associated molecules revealed that the receptor expression trends were consistent with the observed binding patterns. Specifically, integrin subunits known to bind fibronectin (α5 and αv), laminin (α6 and α3), and galectins (α3) were all more prevalent on the metastasis-derived line, while those associated with collagens (α1 and α2) were relatively higher on the primary tumor-derived line (Fig. 6a). Nonetheless, the surface expression trends were consistent for the other TMet and M lines as well (Supplementary Fig. S6a). Furthermore, within a given cell line, we observed relatively homogeneous surface expression of the metastasis-associated integrins (Supplementary Fig. S6b) suggesting that variations in adhesion between lines are due to global increases in surface receptor expression, rather than binding patterns of select subpopulations.
Immunohistochemistry revealed that these integrins were also present in the metastases of mice bearing autochthonous tumors, but not the adjacent tissue (Fig. 6b). The finding that the transcriptional levels of the integrins do not agree with the adhesion trends suggests that post-transcriptional regulation, post-translational modifications such as altered glycosylation, or alterations in activation state of the integrins are likely responsible for the changes in adhesion. Thus, by utilizing our platform that investigates specific ECM binding rather than receptor gene or protein expression, we are able to identify candidate ECM interactions that might otherwise have been overlooked.
To examine which candidate receptor/ECM interactions may participate in the observed binding patterns, we performed in silico network mapping of the metastasis-associated ECM molecules using GeneGO software (Metacore) of manually curated molecular interactions. We generated a network map that we termed the Lung Adenocarcinoma Metastasis (LAM) network that has a greatest disease association with ‘Neoplasm Metastasis’ (P = 1.094 × 10−45, hypergeometric test, Fig. 7a, Supplementary Fig. S7). A network generated using the same parameters but with the primary-tumor-associated molecules did not exhibit any disease association with metastasis (Supplementary Fig. S8). Analysis of the LAM network identified integrin α3β1 as the surface receptor with the greatest number of edges (Fig. 7a). Based on this finding, we performed a knockdown of both the α3 and β1 subunits (ITGA3 and ITGB1, respectively) using short-hairpin-mediated RNA-interference (Supplementary Fig. S9). Knockdown of these genes in the metastatic line, 393M1, resulted in reduced adhesion to the metastasis-associated molecules in vitro when compared to the control hairpin targeting the firefly luciferase gene (Fig. 7c).
We next assessed whether this integrin dimer plays a role in metastatic seeding in vivo. Thus, we conducted experimental metastasis assays by intrasplenic injection of 393M1-shα3 or 393M1-shFF cells into wild type mice, and monitoring for liver tumor formation. We found that mice injected with the 393M1-shα3 cells formed fewer tumor nodules than the controls (Fig. 7d–f). Taken together, these findings suggest that the α3β1 integrin dimer plays a role in adhesion of metastatic cells to the metastasis-associated ECM molecules and in metastatic seeding.
Based on the in vitro adhesion data and in vivo mouse findings, we sought to explore the role of the metastasis-associated ECM molecules in human samples. Using Oncomine32, a human genetic dataset analysis tool, we examined the correlation of ECM gene expression and disease severity (e.g. clinical stage or the presence of metastases). Results of these queries demonstrate that increased expression of LGALS3 or LGALS8 (galectin-3 and galectin-8, respectively) correlate with increased clinical stage or the presence of metastases (Fig. 8a). We next investigated whether galectin-3 protein is present at higher levels in malignant human lung tumors compared to benign non-neoplastic human lung tissue using samples taken from lungs and lymph nodes of patients. Staining for galectin-3 in human tissue microarrays revealed a higher presence of the molecule in lymph nodes of patients with malignant disease (88%) compared to those without cancer (38%) (Fig. 8b). Furthermore, there was a higher fraction of galectin-3-positive lymph nodes (88%) than positive primary lung tumor samples (47%), confirming its association with the metastatic site over the primary tumor (P < 0.05, Fisher’s Exact Test). Thus, the ECM microarrays were capable of identifying interactions associated with metastasis in human lung cancer.
Our ECM microarrays provide a high-throughput multiplexed platform capable of measuring a variety of cellular responses to ECM. Here, we show they are capable of identifying adhesion patterns that differentiate metastatic populations from primary tumors. We found that metastatic lung cancer cells preferentially bind to fibronectin in combination with laminin, galectin-3, or galectin-8 compared to cells derived from primary tumors. These changes in adhesion correlate with changes in surface presentation of various integrins. In particular, α3β1 mediates adhesion to these molecules in vitro and permits metastatic seeding in vivo. Furthermore, metastases derived from both a genetically-engineered mouse lung cancer model and from human lung cancers express the metastasis-associated ECM molecules. It is worth noting that the combinations of these ECM components elicited the strongest effects, highlighting the importance of using a platform that is capable of measuring responses to more than individual molecules.
Galectins are a class of lectins that bind β-galactosides and can associate with other ECM molecules such as fibronectin33. Galectin-3 is associated with metastasis in a variety of cancers34–35 and can bind to the oncofetal Thomsen-Friedenreich antigen, a carbohydrate antigen overexpressed by many carcinomas36. Our platform confirmed its importance in lung adenocarcinoma, and also identified galectin-8 as having similar importance. While galectin-8 is known to affect adhesion of cells to other matrix molecules, its role in cancer and metastasis has been less clear as it has been found to have both a positive and negative association with adhesion and tumorigenesis37–38. Using the ECM microarrays, we showed that binding to galectin-8 in combination with fibronectin is strongly associated with metastatic progression in lung adenocarcinoma.
Furthermore, in addition to many collagens, we found that loss of adhesion to osteopontin accompanied metastatic progression (Supplementary Fig. S3a–c). Osteopontin levels correlate with prognosis in patients with metastatic disease39, and secretion of osteopontin by primary tumors results in mobilization of bone marrow-derived stromal precursors that help establish the metastatic niche40. In addition to confirming the presence of the metastatic molecules at the sites of metastases, we found that the invasive portions of primary tumors and the invasive front of the metastases secrete osteopontin (Supplementary Fig. S5b). A metastatic tumor line also produces more osteopontin than its corresponding primary (Fig. 5a). These findings suggest that while some primary tumors may activate bone marrow cells by secreting osteopontin, in our model metastatic cells may contribute to this recruitment at a comparable or higher level than the instigating primaries, despite their own loss of adhesion to the immobilized molecule.
The use of gene expression signatures for patient stratification in the clinic has become more widespread41–45, but while genomic approaches have been beneficial for identifying candidate genes, the diversity of findings makes the development of broad therapeutic options seem nearly impossible. By assaying for conserved mechanisms at the phenotypic level, however, relevant targets can be identified and therapeutics can be developed for a broad spectrum of patients. Our results highlight the utility of phenotypic screening approaches for identifying clinical biomarkers. While we identify α3β1 integrin as a therapeutic target, we also demonstrate that the adhesion signatures generated by the ECM microarrays are capable of differentiating between genetically similar populations with varying metastatic potential. Furthermore, no increase in the mRNA levels of the galectins or their receptors was observed by gene expression microarrays in the M lines (Fig. 5 and ref29), despite the association of these molecules with metastasis. The presence of galectin-3 and galectin-8 in human samples (Fig. 8) demonstrates the relevance of this platform to human disease, and thus, we envision that these arrays may be a useful clinical tool for stratification of cancer patients beyond traditional TNM staging.
The value of the ECM microarray platform extends beyond the specific application of cancer metastasis. While this study documents the ability to profile adhesion patterns, cells bound to the arrays can be kept in culture for multiple days to monitor long-term responses to ECM such as cell death, proliferation, and alterations in gene or protein expression. Toward that end, one could use multiplexed antibody staining to probe the effects of ECM on stem cell differentiation or activation. Orthogonal screens can be performed to look at the effects of growth factors, small molecules, or RNA-interference agents in the context of ECM. Reduction of requisite cell numbers can be achieved using miniaturized arrays to screen rare cell populations such as circulating tumor cells or cancer stem cells and to help expand those populations in vitro for further biological studies. Overall, the ECM microarrays will enhance our ability to study a host of questions as they pertain to both basic biological and clinical settings.
Cell lines have been described29. Briefly, tumor initiation was achieved using intratracheal injection of lentiviral Cre recombinase. Tumors were resected, digested, and plated onto tissue culture treated plastic to generate cell lines29. Cell lines were subsequently cultured in DMEM, 10% FBS, penicillin/streptomycin, and glutamine. These lines were derived from both primary lung tumors and their metastases. See Supplementary Table S2 for nomenclature regarding cell line origins.
All animal procedures were performed in accordance with the MIT Institutional Animal Care and Use Committee under protocol 0211-014-14. Cell injection studies were performed in B6129SF1/J mice (Jackson Laboratory, Stock Number 101043). Intrasplenic injections were performed using 5×105 cells resuspended in 100µL of PBS and injected into the tip of the spleen following existing protocols29. Animals were anesthetized with avertin prior to surgery. Fur was removed from the animals and they were sterilized with Betadine and 70% ethanol. The spleen was exteriorized following incisions in the skin and body wall. Cells were injected into the end of the spleen with a 27gauge syringe and allowed to travel into circulation for two minutes. Spleens were then excised from the animals following cauterization of the splenic vessels. The muscle wall was closed using 5-0 dissolvable sutures, and the skin was closed using 7mm wound clips (Roboz). Mice were euthanized 2.5-4 weeks following injection, and their livers were excised. Quantification of surface nodules and imaging of livers was performed using a dissection microscope. Tissues were embedded in paraffin following fixation in 4% paraformaldehyde and stained using hematoxylin and eosin.
Vantage acrylic slides (CEL Associates VACR-25C) were coated with polyacrylamide by depositing prepolymer containing Irgacure 2959 photoinitiator (Ciba) between the slide and a glass coverslip22. Following polymerization, slides were soaked in ddH2O and the coverslips were removed. Slides were allowed to dry prior to molecule deposition. Slides were spotted using a DNA Microarray spotter (Cartesian Technologies Pixsys Microarray Spotter and ArrayIt 946 Pins). 768 combinations were spotted in replicates of five. Rhodamine dextran (Invitrogen) was spotted as negative controls and for use in image alignment. The following molecules were used: Collagen I (Millipore), Collagen II (Millipore), Collagen III (Millipore), Collagen IV (Millipore), Collagen V (BD Biosciences), Collagen VI (BD Biosciences), Fibronectin (Millipore), Laminin (Millipore), Merosin (Millipore), Tenascin-R (R&D Systems), Chondroitin Sulfate (Millipore), Aggrecan (Sigma), Elastin (Sigma), Keratin (Sigma), Mucin (Sigma), Superfibronectin (Sigma), F-Spondin (R&D Systems), Nidogen-2 (R&D Systems), Heparan Sulfate (Sigma), Biglycan (R&D Systems), Decorin (R&D Systems), Galectin 1 (R&D Systems), Galectin 3 (R&D Systems), Galectin 3c (EMD Biosciences), Galectin 4 (R&D Systems), Galectin 8 (R&D Systems), Thrombospondin-4 (R&D Systems), Osteopontin (R&D Systems), Osteonectin (R&D Systems), Testican 1 (R&D Systems), Testican 2 (R&D Systems), Fibrin (Sigma), Tenascin-C (R&D Systems), Nidogen-1 (R&D Systems), Vitronectin (R&D Systems), Rat Agrin (R&D Systems), Hyaluronan (R&D Systems), Brevican (R&D Systems). The laminin used is Millipore Catalog No. AG56P, and is a mixture of human laminins that contain the beta1 chain. Source plates used in the spotter were prepared using a Tecan liquid handler. Molecules were prepared at a concentration of 200µg/mL using a buffer described previously 22. Slides were stored in a humidity chamber at 4°C prior to use.
Slides were washed in PBS and treated with UV prior to seeding cells. They were placed in a seeding device that holds the top surface of the slides flush with bottom of the well. 400,000 cells were seeded on each slide in 6mL of serum-free medium (DMEM and penicillin/streptomycin). Cells were allowed to attach for two hours at 37°C. After attachment, slides were washed three times, transferred to quadriperm plates (NUNC, 167063), and new medium was added (DMEM, 10%FBS, penicillin/streptomycin, and glutamine). Slides were left at 37°C for two additional hours prior to removal for staining. Slides were washed twice with PBS and fixed with 4% paraformaldehyde. Nuclei were stained using Hoechst (Invitrogen) in combination with 0.1% Triton-X and PBS. Slides were mounted with Fluoromount-G (Southern Biotech 0100-01) and stored at 4°C prior to imaging. Slides were imaged using a Nikon Ti-E inverted fluorescence microscope and NIS Elements Software (Nikon). The entire slide was scanned and images stitched using that software. Image manipulation and analysis was performed in MATLAB (Mathworks) and quantification of nuclei was performed using CellProfiler30. Clustering analysis was performed using Spotfire (Tibco). Replicate spots on each slide were averaged and those whose values were greater than one standard deviation above or below the mean of the replicates were excluded. Slides were normalized to the mean of their non-zero adhesion values. Clustering was performed based on Euclidean distances using Spotfire (TIBCO) with the Hierarchical Clustering algorithm (normalized adhesion > 0.01).
in vitro ECM adhesion tests were performed using 96-well-plates (Corning 3603). Plates were coated with 20µg/mL of fibronectin alone or 20µg/mL of fibronectin and 20µL/mL of the second molecule in PBS overnight at 4°C. Plates were then blocked with 1wt% BSA at room temperature for 1hr. Plates were allowed to dry prior to adding 2×104 cells/well in warm serum-free DMEM. Cells were allowed to adhere for 1hr at 37°C and shaken every 15minutes to ensure uniform seeding. Cells were washed, fixed with 4% paraformaldehyde, and stained with Hoechst (invitrogen). Wells were imaged using a Nikon Ti-E inverted epifluorescent microscope and analyzed with Nikon Elements software.
Western blot analysis of ECM molecules was performed with the following antibodies: galectin-3 (Abcam ab53082, 1:500), galectin-8 (Abcam ab69631, 1:500), osteopontin (Abcam ab8448, 1:2000), fibronectin (Abcam ab2413, 1:1000), laminin (Abcam b11575, 1:1000), collagen I (Abcam ab34710, 1:5000), and α-tubulin (Cell Signaling 2125, 1:1000). Immunohistochemistry of ECM molecules was performed with the following antibodies: galectin-3 (Abcam ab53082, 1:500), galectin-8 (Abcam ab69631, 1:75), osteopontin (Abcam ab8448, 1:200), laminin (Abcam ab11575, 1:100), fibronectin (Millipore AB2033, 1:80), Hmga2 (Biocheck 59170AP, 1:1000), collagen I (Abcam ab34710, 1:500),collagen VI (Abcam ab6588, 1:100). Integrin staining was performed using the following antibodies: integrin αv (Millipore AB1930, 1:200), integrin α5 (Chemicon AB1928, 1:200), integrin α3 antibody was a gift from JML. Tissue microarrays were acquired from LifeSpan Biosciences (LS-SLUCA50), and were stained with the same galectin-3 antibody. Murine tissues were harvested from KrasLSL-G12D, p53flox/flox mice27–29. IHC was performed following resection from mice, fixation in formalin, and embedding in paraffin. Flow cytometry analysis of integrin expression was performed using the following antibodies: integrin α5 (Abcam and BioLegend-clone 5H10-27, 1:100), integrin αv (BD-clone RMV-7, 1:100), integrin α6 (BD and BioLegend-clone GoH3, 1:100), integrin α3 (R&D, 1:100), integrin α1 (BD-clone Ha31/8 and BioLegend-clone HMα1, 1:100) and integrin α2 (BD-clone HMα2, 1:100).
Cell lysates were harvested using Trizol (Sigma). Chloroform extraction was performed followed by RNA purification using Qiagen RNeasy spin columns. Lysates were analyzed for RNA integrity and prepared with Affymetrix GeneChip WT Sense Target Labelling and Control Reagents kit, followed by hybridization to Affymetrix Mouse 3’ Arrays (Mouse 430A 2.0) Lysates used for gene expression microarrays were harvested at the same time as the ECM microarrays were seeded to ensure minimal variability introduced by cell culture. R/Bioconductor software was used to process array images. Unsupervised hierarchical clustering analysis was performed in Spotfire (Tibco) for all probesets with variance>0.5 and expression>3.0 using Euclidean distances. Datasets are publically available from NCBI under accession number GSE40222.
miR30-based shRNAs targeting integrins β1 (TGCTGTTGACAGTGAGCGCGGCTCTCAAACTATAAAGAAATAGTGAAGCCACAGATGTATTTCTTTATAGTTTGAGAGCCTTGCCTACTGCCTCGGA), α3 (TGCTGTTGACAGTGAGCGCCGGATGGACATTTCAGAGAAATAGTGAAGCCACAGATGTATTTCTCTGAAATGTCCATCCGTTGCCTACTGCCTCGGA), or control firefly luciferase (AAGGTATATTGCTGTTGACAGTGAGCGAGCTCCCGTGAATTGGAATCCTAGTGAAGCCACAGATGTAGGATTCCAATTCAGCGGGAGCCTGCCTACTGCCTCG) were designed using the shRNA retriever software (http://katahdin.cshl.edu/homepage/siRNA/RNAi.cgi?type=shRNA), synthesized (IDT, Coralville, Iowa), and then cloned into the MSCV-ZSG-2A-Puro-miR30 vector48. Packaging of retrovirus and transduction of cells was done as described previously49.
N.E.R.F. and S.N.B. wrote the manuscript. N.E.R.F., M.M.W., and S.N.B. designed the studies. N.E.R.F. and D.F.B.M. designed and produced the ECM microarrays. M.M.W. generated the initial cell lines. N.E.R.F. and M.J.X. performed the ECM microarray seeding. J.M.L. designed and synthesized the hairpins and retroviruses. N.E.R.F. performed all other experiments and all data analysis. All authors contributed conceptual advice and edited the manuscript.
FN2COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
The authors thank H. Fleming, S. Katz, L. Ingaharro, and all other members of the S.B. lab for their insightful discussions and general research assistance. They also thank the Swanson Biotechnology Center Core Facilities and the David H. Koch Institute and, in particular, M. Brown, C. Whittaker, G. Paradis, M. Luo, S. Levine, Y. Xue, and C. Kim-Kiselak. M.M.W. is funded by NIH grant K99-CA151968. J.M.L. was supported by a National Research and Service Award (NRSA) from the NIH, and a postdoctoral fellowship from the National Cancer Center. This work was supported by Stand Up to Cancer (SU2C/AACR), the Koch Institute CTC Project, and the Harvard Stem Cell Institute (SG-0046-08-00), the National Cancer Institute (U54CA126515 and U54CA112967), the Howard Hughes Medical Institute (of which R.O.H., T.E.J., and S.N.B. are Investigators), and the Ludwig Center at MIT.
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