Aiello Katherine A, Ponnapalli Sri Priya, Alter Orly
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah 84112, USA.
Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112, USA.
APL Bioeng. 2018 Sep;2(3). doi: 10.1063/1.5037882. Epub 2018 Sep 19.
DNA alterations have been observed in astrocytoma for decades. A copy-number genotype predictive of a survival phenotype was only discovered by using the generalized singular value decomposition (GSVD) formulated as a comparative spectral decomposition. Here, we use the GSVD to compare whole-genome sequencing (WGS) profiles of patient-matched astrocytoma and normal DNA. First, the GSVD uncovers a genome-wide pattern of copy-number alterations, which is bounded by patterns recently uncovered by the GSVDs of microarray-profiled patient-matched glioblastoma (GBM) and, separately, lower-grade astrocytoma and normal genomes. Like the microarray patterns, the WGS pattern is correlated with an approximately one-year median survival time. By filling in gaps in the microarray patterns, the WGS pattern reveals that this biologically consistent genotype encodes for transformation via the Notch together with the Ras and Shh pathways. Second, like the GSVDs of the microarray profiles, the GSVD of the WGS profiles separates the tumor-exclusive pattern from normal copy-number variations and experimental inconsistencies. These include the WGS technology-specific effects of guaninecytosine content variations across the genomes that are correlated with experimental batches. Third, by identifying the biologically consistent phenotype among the WGS-profiled tumors, the GBM pattern proves to be a technology-independent predictor of survival and response to chemotherapy and radiation, statistically better than the patient's age and tumor's grade, the best other indicators, and promoter methylation and mutation. We conclude that by using the complex structure of the data, comparative spectral decompositions underlie a mathematically universal description of the genotype-phenotype relations in cancer that other methods miss.
几十年来，人们一直在星形细胞瘤中观察到DNA改变。通过使用公式化为比较光谱分解的广义奇异值分解（GSVD），才发现了一种预测生存表型的拷贝数基因型。在这里，我们使用GSVD来比较患者匹配的星形细胞瘤和正常DNA的全基因组测序（WGS）图谱。首先，GSVD揭示了全基因组范围内的拷贝数改变模式，该模式受最近通过微阵列分析的患者匹配胶质母细胞瘤（GBM）以及低级别星形细胞瘤和正常基因组的GSVD所揭示的模式限制。与微阵列模式一样，WGS模式与大约一年的中位生存时间相关。通过填补微阵列模式中的空白，WGS模式揭示了这种生物学上一致的基因型通过Notch以及Ras和Shh途径编码转化。其次，与微阵列图谱的GSVD一样，WGS图谱的GSVD将肿瘤特异性模式与正常拷贝数变异和实验不一致性区分开来。这些包括全基因组中鸟嘌呤 - 胞嘧啶含量变化的WGS技术特异性效应，这些效应与实验批次相关。第三，通过在WGS分析的肿瘤中识别生物学上一致的表型，GBM模式被证明是生存以及对化疗和放疗反应的技术独立预测指标，在统计学上优于患者年龄和肿瘤分级这两个最佳的其他指标，以及启动子甲基化和突变。我们得出结论，通过利用数据的复杂结构进行比较光谱分解，构成了对癌症中基因型 - 表型关系的数学通用描述，而其他方法则忽略了这一点。
