Developing Mathmatical Framework to Uncover the Sequence of Genetic Events During

认识到癌症是一种由遗传变异积累引起的疾病， 大规模注释所有人类癌症的癌症基因组。当结合 计算方法，可以区分统计上显着的，从背景的复发事件 尽管高分辨率数据集中的“噪音”，这些癌症基因组调查产生了特异性的分子画像， 并且在多个样本集之间高度一致。在这个项目中，我们将把这些 新兴的，大型横截面数据集与一种新的数学模型来预测基因序列 肿瘤发生过程中的事件。我们的预测将使用一个进化模型的动态内， 可能的突变网络。当应用于~ 70例晚期结直肠癌时，该算法正确地 重建先前描述的结直肠肿瘤APC -> Ras TP 53突变序列 发展我们将首先完善我们的数学模型，以包括其他变量，如 异质性、上位性和不同肿瘤类型之间群体结构的差异（目的1）。 然后，我们将其应用于原发性胶质母细胞瘤（Aim 2）和急性白血病（Aim 3）的基因组数据集， 预测相关遗传事件的序列，并在小鼠中检查这些遗传事件的序列如何改变。 事件影响肿瘤形成。

The recognition of cancer as a disease caused by the accumulation of genetic alterations has motivated large-scale efforts to annotate the cancer genome for all human cancers. When combined with computational approaches that can distinguish statistically significant, recurrent events from the background "noise" in high-resolution datasets, these cancer genome surveys yield molecular portraits which are specific for each cancer type and highly consistent across multiple sample sets. In this project, we will link these emerging, large cross-sectional datasets with a novel mathematical model to predict the sequence of genetic events during tumorigenesis. Our predictions will use an evolutionary model of the dynamics within a network of possible mutations. When applied to ~ 70 advanced colorectal cancers, this algorithm correctly reconstructs the sequence of APC -> Ras ¿¿TP53 mutations previously described for colorectal tumor development. We will first refine our mathematical model to include additional variables such as heterogeneity, epistasls, and differences in the population structure between different tumor types (Aim 1). We will then apply it to genomic datasets for primary glioblastoma (Aim 2) and acute leukemia (Aim 3), predict the sequence of associated genetic events, and examine in mice how the sequence of these genetic events affects tumor formation.