Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.

中文翻译：

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使用多因素分析的个体化多组学路径偏差评分。

正常组织的恶性进展通常由复杂的体细胞变化网络驱动，包括基因突变、拷贝数异常、表观遗传变化和转录重编程。为了描绘与临床结果相关的异常多组学肿瘤特征，我们提出了一种基于称为padma的多因素分析框架的以通路为中心的新型工具。使用多组学共识表示，padma量化和表征个体化通路特异性多组学偏差及其潜在驱动因素，相对于抽样人群。我们演示了padma的实用性将患者结果与乳腺癌和肺癌临床可操作途径中复杂的遗传、表观遗传和转录组学扰动相关联。