Developing Mathmatical Framework to Uncover the Sequence of Genetic Events During

开发数学框架来揭示遗传事件的序列

• 批准号: 8566840
• 负责人: Franziska Michor
• 金额: $ 62.11万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8566840
• 关键词: Acute Myelocytic Leukemia; Acute leukemia; Affect; Algorithms; Atlases; Biological Markers; Brain Neoplasms; Cells; Colorectal Cancer; Colorectal Neoplasms; Computational algorithm; Data; Data Analyses; Data Set; Development; Disease; Event; Gene Transfer; Genetic; Genetic Anticipation; Genomics; Glioblastoma; Glioma; Hematopoietic Neoplasms; Heterogeneity; Human; Instruction; Knowledge; Laboratories; Link; Lung Neoplasms; Malignant Neoplasms; Memorial Sloan-Kettering Cancer Center; Mining; Modeling; Molecular; Mus; Mutation; Noise; Oncogenes; Patients; Population; Portraits; Publishing; Recurrence; Resolution; Role; Sampling; Screening for cancer; Somatic Cell; Staging; Structure; Surveys; TP53 gene; Testing; The Cancer Genome Atlas; advanced disease; base; cancer genome; cancer type; experience; leukemia; mathematical model; mouse model; novel; tumor; tumorigenesis; validation studies

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.

认识到癌症是一种由基因改变的积累引起的疾病，这促使人们