Computational approaches for the analyses of spatial profiling technologies

目的1：针对高度复杂的空间蛋白质组学数据，对图像区域编码者进行无监督训练。我们将首先确定最佳编码器以获得图像区域表示。这种编码将在完全无监督的方法中学习，以使用空间成像数据中的所有可用区域，而不需要任何临床标签或来自任何预处理步骤的任何单个细胞位置信息。学习的编码将用于目标2。目标2：跨癌症类型的可解释的差异分析，以识别与临床特征相关联的空间拓扑特征，如生存结果。我们将使用可解释的机器学习方法从以学习的图像区域编码表示的图像中预测临床特征。预测方法的可解释性将有助于可视化与不同临床类别相关的图像区域，如预后良好和不良。编码空间中重要图像区域的共性或差异将识别临床结果中潜在的关键相似或差异，这可用于识别肿瘤免疫逃避机制，并有助于诊断和治疗。初步数据。我们为Codex和IMC等空间成像技术从结直肠癌(432个样本)、头颈癌(308个样本)和乳腺癌(681个样本)的外部数据库中收集了空间蛋白质组学图像和相应的患者生存。我们也有NCI内部收集的肺腺癌Codex图像。

Aim 1: Unsupervised training of image region encoder for highly-multiplexed spatial proteomics data. We will first identify the optimal encoder to obtain image region representations. This encoding will be learned in a completely unsupervised approach to use all the available regions in the spatial imaging data without requiring any clinical labels, or any individual cell location information from any preprocessing step. The learned encoding will be used in Aim 2. Aim 2: Interpretable differential analysis across cancer types to identify spatial topological features associated with clinical characteristics like survival outcomes. We will use interpretable machine learning methods to predict clinical characteristics from images represented in terms of learned image region encodings. The interpretability of the prediction method will help in visualizing image regions associated with different clinical categories, like good and poor prognosis. The commonality or differences of important image regions in the encoded space will identify underlying key similarities or differences across clinical outcomes, which could be used to identify tumor immune evasion mechanisms and help in diagnosis and therapy. Preliminary Data. We have collected spatial proteomics images and corresponding patient survival from external databases for colorectal cancer (432 samples), head and neck cancer (308 samples) and breast cancer (681 samples) for spatial imaging technologies like CODEX and IMC. We also have Lung adenocarcinoma CODEX images collected internally at NCI.