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.