Mapping lung tumor cell drug responses as a function of matrix context and genotype using cell microarrays

Carcinoma progression is influenced by interactions between epithelial tumor cells and components of their microenvironment. In particular, cell–extracellular matrix (ECM) interactions are known to drive tumor growth, metastatic potential, and sensitivity or resistance to therapy. Yet the intrinsic complexity of ECM composition within the tumor microenvironment remains a barrier to comprehensive investigation of these interactions. We present here a high-throughput cell microarray-based approach to study the impact of defined combinations of ECM proteins on tumor cell drug responses. Using this approach, we quantitatively evaluated the effects of 55 different ECM environments representing all single and two-factor combinations of 10 ECM proteins on the responses of lung adenocarcinoma cells to a selection of cancer-relevant small molecule drugs. This drug panel consisted of an alkylating agent and five receptor tyrosine kinase inhibitors. We further determined that expression of the neuroendocrine transcription factor ASCL1, which has been previously associated with poor patient outcome when co-expressed with the RET oncogene, altered cell responses to drugs and modulated cleavage of the pro-apoptotic protein caspase-3 depending on ECM context. Our results suggest that co-expression of specific ECM proteins with known genetic drivers in lung adenocarcinoma may impact therapeutic efficacy. Furthermore, this approach could be utilized to define the molecular mechanisms by which cell–matrix interactions drive drug resistance through integration with clinical cell samples and genomics data.