Cancer cells display massive dysregulation of key regulatory pathways due to now well-catalogued mutations and other DNA-related aberrations. Moreover, enormous heterogeneity has been commonly observed in the identity, frequency and location of these aberrations across individuals with the same cancer type or subtype, and this variation naturally propagates to the transcriptome, resulting in myriad types of dysregulated gene expression programs. Many have argued that a more integrative and quantitative analysis of heterogeneity of DNA and RNA molecular profiles may be necessary for designing more systematic explorations of alternative therapies and improving predictive accuracy. We introduce a representation of multi-omics profiles which is sufficiently rich to account for observed heterogeneity and support the construction of quantitative, integrated, metrics of variation. Starting from the network of interactions existing in Reactome, we build a library of "paired DNA- RNA aberrations" that represent prototypical and recurrent patterns of dysregulation in cancer; each two-gene "Source-Target Pair" (STP) consists of a "source" regulatory gene and a "target" gene whose expression is plausibly "controlled" by the source gene. The STP is then "aberrant" in a joint DNA-RNA profile if the source gene is DNA-aberrant (e.g., mutated, deleted, or duplicated), and the downstream target gene is "RNA-aberrant", meaning its expression level is outside the normal, baseline range. With M STPs, each sample profile has exactly one of the 2M possible configurations. We concentrate on subsets of STPs, and the corresponding reduced configurations, by selecting tissue-dependent minimal coverings, defined as the smallest family of STPs with the property that every sample in the considered population displays at least one aberrant STP within that family. These minimal coverings can be computed with integer programming. Given such a covering, a natural measure of cross-sample diversity is the extent to which the particular aberrant STPs composing a covering vary from sample to sample; this variability is captured by the entropy of the distribution over configurations. We apply this program to data from TCGA for six distinct tumor types (breast, prostate, lung, colon, liver, and kidney cancer). This enables an efficient simplification of the complex landscape observed in cancer populations, resulting in the identification of novel signatures of molecular alterations which are not detected with frequency-based criteria. Estimates of cancer heterogeneity across tumor phenotypes reveals a stable pattern: entropy increases with disease severity. This framework is then well-suited to accommodate the expanding complexity of cancer genomes and epigenomes emerging from large consortia projects.

中文翻译：

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具有成对的DNA-RNA畸变的肿瘤多样性的有效表征

由于现在已被广泛编录的突变和其他与DNA有关的畸变，癌细胞显示出关键调节途径的大量失调。此外，通常在具有相同癌症类型或亚型的个体中，在这些畸变的身份，频率和位置中观察到极大的异质性，并且这种变异自然地传播到转录组，导致多种类型的基因表达程序失调。许多人认为，对于设计替代疗法的更系统的探索并提高预测准确性，可能需要对DNA和RNA分子图谱的异质性进行更全面和定量的分析。我们介绍了一种多组学谱图的表示形式，该图谱足够丰富，可以说明观察到的异质性，并支持定量，集成的变化指标。从Reactome中存在的相互作用网络开始，我们建立了一个“成对的DNA-RNA畸变”文库，该库代表了癌症失调的典型和复发模式。每个两个基因的“源-靶对”（STP）由一个“源”调节基因和一个“靶”基因组成，其表达可能受到源基因的“控制”。如果源基因是DNA异常（例如，突变，缺失或重复），而下游目标基因是“ RNA异常”，则STP在联合DNA-RNA谱中是“异常”的，这意味着其表达水平是超出正常基准范围。对于M个STP，每个样本配置文件都具有2M种可能的配置中的一种。我们专注于STP的子集，通过选择依赖于组织的最小覆盖物（定义为STP的最小家族），具有相应的简化配置，该属性具有以下特征：所考虑的群体中的每个样本在该家族中均显示至少一个异常STP。这些最小覆盖率可以使用整数编程来计算。给定这样一个覆盖范围，跨样本多样性的自然衡量标准就是构成覆盖范围的特定异常STP在不同样本之间的变化程度；这种可变性是通过配置的分布熵来捕获的。我们将该程序应用于来自TCGA的六种不同肿瘤类型（乳腺癌，前列腺癌，肺癌，结肠癌，肝癌和肾癌）的数据。这样可以有效简化在癌症人群中观察到的复杂情况，导致鉴定了基于频率的标准无法检测到的分子变化的新特征。跨肿瘤表型的癌症异质性估计揭示了一个稳定的模式：熵随疾病严重程度而增加。然后，该框架非常适合于适应从大型财团项目中出现的癌症基因组和表观基因组不断增长的复杂性。