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Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.

BACKGROUND: SOX2 is an embryonic developmental transcription factor, which is important in the development of the respiratory tract. SOX2 overexpression is associated with aggressive disease in several tumor types. However, SOX2 overexpression and gene amplification associates with favorable outcome in lung squamous cell carcinomas (SCC) and dissimilar results have been reported in lung adenocarcinomas (ADC). The aim of the present study was to evaluate SOX2 expression in NSCLC and determine the relationship with clinico-pathological variables and outcome. METHODS: SOX2 protein levels were measured in tissue microarrays (TMAs) containing FFPE samples from two independent lung cancer cohorts (n = 340 & 307) using automated quantitative immunofluorescence (QIF). Assay validation was performed using FFPE preparations of cell lines with known SOX2 expression. Associations of SOX2 levels with main clinico-pathological characteristics and with overall survival were studied using uni-and multivariate analysis. RESULTS: SOX2 levels were higher in patients with SCC than in ADC in both cohorts (p value