Data-driven inference of regulators for cytokine-mediated tumor killing

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Aim 1, Discover new cytokines promoting tumor progression through data-integrative analysis. We will comprehensively integrate clinical genomics data from cancer immunotherapy studies to identify new soluble proteins whose gene expression levels are significantly associated with patient clinical outcomes in many cohorts. Then, we will validate the top candidates' in-vivo impact on tumor progression using gene over-expression in mouse tumors. For validated secreted proteins, we will generate treatment response profiles to enrich the CytoSig framework. Aim 2. Develop an algorithm to identify secreted protein signaling in ST data. Many computational methods exist for studying ligand-receptor interactions from bulk or single-cell transcriptomics data. However, the receptors for most secreted proteins (1903 by estimation) are unknown. Also, many non-receptor proteins may serve the essential regulators or indicators of secreted protein functions. To study signaling activities for a broad set of secreted proteins, we will develop spatial pattern detection algorithms to identify functional-relevant genes whose expression patterns have positive or negative spatial correlations with the coding gene of secreted proteins. Then, top predicted regulators will be validated using the in-vivo models established in Aim 1.