Developing Mathmatical Framework to Uncover the Sequence of Genetic Events During

人们认识到癌症是一种由基因改变积累引起的疾病，这促使人们 对所有人类癌症的癌症基因组进行注释的大规模努力。当与 能够区分统计上有意义的、反复发生的事件和背景的计算方法 “噪音”在高分辨率数据集中，这些癌症基因组调查产生了特定的分子肖像 对于每种癌症类型，并且在多个样本组中高度一致。在这个项目中，我们将把这些 使用一种新的数学模型预测基因序列的新兴大型横截面数据集 肿瘤发生过程中的事件。我们的预测将使用一种动态的进化模型 可能突变的网络。当应用于约70例晚期结直肠癌时，该算法正确 重建先前描述的结直肠肿瘤APC-&GT；RAS？TP53突变序列 发展。我们将首先改进我们的数学模型，以包括其他变量，如 不同肿瘤类型之间的异质性、上位性和群体结构差异(目标1)。 然后我们将把它应用于原发胶质母细胞瘤(AIM 2)和急性白血病(AIM 3)的基因组数据集， 预测相关基因事件的序列，并在小鼠身上检查这些基因事件的序列是如何 事件会影响肿瘤的形成。

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.