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

目的1、通过数据整合分析发现新的促进肿瘤进展的细胞因子。我们将全面整合来自癌症免疫治疗研究的临床基因组学数据，以确定新的可溶性蛋白质，其基因表达水平与许多队列中的患者临床结局显著相关。然后，我们将使用小鼠肿瘤中的基因过表达来验证最佳候选物对肿瘤进展的体内影响。对于经验证的分泌蛋白，我们将生成治疗反应谱以富集CytoSig框架。目标2.开发一种算法来识别ST数据中的分泌蛋白信号。存在许多计算方法用于从大量或单细胞转录组学数据研究配体-受体相互作用。然而，大多数分泌蛋白的受体（估计为1903）是未知的。此外，许多非受体蛋白质可充当分泌蛋白质功能的基本调节剂或指示剂。为了研究一组广泛的分泌蛋白的信号传导活性，我们将开发空间模式检测算法来识别功能相关基因，其表达模式与分泌蛋白的编码基因具有正或负的空间相关性。然后，将使用目标1中建立的体内模型验证最佳预测调节剂。

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.