RESUMEN
Although immune checkpoint inhibitor-based therapy has shown promising results in non-small cell lung cancer patients with high programmed death-ligand 1 expression, not all patients respond to therapy. The tumor microenvironment (TME) is complex and heterogeneous, making it challenging to understand the key agents and features that influence response to therapies. In this study, we leverage multiplex fluorescent immunohistochemistry to quantitatively assess interactions between tumor and immune cells in an effort to identify patterns occurring at multiple spatial levels of the TME. To do so, we introduce several computational methods novel to a data set of 1,269 multiplex fluorescent immunohistochemistry images from a cohort of 52 patients with metastatic non-small cell lung cancer. With the spatial G-cross function, we quantify the degree of cell interaction at an entire image level, where we see significantly increased activity of cytotoxic T cells and helper T cells with epithelial tumor cells in responders to immune checkpoint inhibitor-based (P = .022 and P < .001, respectively) and decreased activity of T-regulatory cells with epithelial tumor cells compared with nonresponders (P = .010). By leveraging spatial overlap methods, we define tumor subregions (which we call the tumor "periphery," "edge." and "center") and discover more localized immune-immune interactions influencing positive response, including those between cytotoxic T cells and helper T cells with antigen presenting cells in these subregions specifically. Finally, we trained an interpretable deep learning model that identified key cellular regions of interest that most influenced response classification (area under the curve = 0.71 ± 0.02). Assessing spatial interactions within these subregions further revealed new insights that were not significant at the whole image level, particularly the elevated association of antigen presenting cells and T-regulatory cells with one another in responder groups (P = .024). Altogether, we demonstrate that elucidating patterns of cell composition and interplay across multiple levels of spatial analyses can improve our understanding of the TME and better differentiate patient responses to immunotherapy.
RESUMEN
Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.