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Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype

机译:数学上通用且生物学上一致的星形细胞瘤基因型编码转化并预测存活表型

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摘要

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 guanine-cytosine 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 MGMT promoter methylation and IDH1 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.
机译:在星形细胞瘤中观察到DNA改变已有数十年了。仅通过将广义奇异值分解(GSVD)公式化为比较光谱分解,即可发现可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)配置文件。首先,GSVD揭示了全基因组拷贝数改变的模式,该模式受最近由微阵列分析的患者匹配的胶质母细胞瘤(GBM)的GSVD和较低级别的星形细胞瘤和正常基因组所发现的模式所限制。像微阵列模式一样,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缺口,WGS模式显示该生物学上一致的基因型编码通过Notch以及Ras和Shh途径的转化。其次,像微阵列图谱的GSVD一样,WGS图谱的GSVD将肿瘤排他性模式与正常拷贝数变异和实验不一致区分开。其中包括与实验批次相关的整个基因组中鸟嘌呤-胞嘧啶含量变化的WGS技术特有效果。第三,通过在WGS谱图鉴定的肿瘤中鉴定生物学上一致的表型,GBM模式被证明是技术,独立于生存和对化学疗法和放射线反应的预测指标,在统计学上优于患者的年龄和肿瘤等级,是其他最佳指标, MGMT启动子甲基化和IDH1突变。我们得出结论,通过使用数据的复杂结构,比较光谱分解是其他方法所忽略的癌症基因型-表型关系的数学通用描述。

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