(PQB5) Reconstruction of Evolutionary Networks using Cross-Sectional Genomic Data

(PQB5)利用横截面基因组数据重建进化网络

• 批准号: 8687270
• 负责人: ANNA LASORELLA
• 金额: $ 51.77万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8687270
• 关键词: Address; Biological; Biological Markers; Brain; Chronic Lymphocytic Leukemia; Collection; Complex; Computer Simulation; Cross-Sectional Studies; Data; Data Set; Deltastab; Diagnostic Neoplasm Staging; Event; Evolution; Experimental Genetics; Genes; Genetic; Genomics; Glioblastoma; Glioma; Heterogeneity; High-Throughput Nucleotide Sequencing; International; Lesion; Link; Malignant Neoplasms; Mesenchymal; Methods; Modeling; Mus; Mutation; Nature; Neoplasm Metastasis; Patients; Pattern; Phase; Process; Recording of previous events; Regimen; Resistance; Route; Sampling; Shapes; Solid Neoplasm; Staging; Subfamily lentivirinae; The Cancer Genome Atlas; Therapeutic; Time; Tumor stage; Ursidae Family; Validation; Work; base; cancer genome; cohort; exome sequencing; genome-wide; high throughput analysis; longitudinal analysis; man; novel; outcome forecast; pressure; public health relevance; reconstruction; teration; tumor; tumor initiation; tumor progression; tumorigenesis; uncontrolled cell growth

DESCRIPTION (provided by applicant): Cancer is a dynamic process that proceeds through the accumulation of genomic alterations. Large sequencing projects have illuminated the complex static landscapes of alterations across a large number of tumors. However, these studies have failed to address the dynamic nature of cancers. Understanding how tumors are shaped by selective pressures bears implications in therapies and prognoses. To address this issue we propose PITCH (Parsimony Inference of Tumor Clone Heterogeneity), a computational model that aims to uncover the evolutionary history of tumors using high throughput genomic data from cross-sectional studies. PITCH identifies traces of older clones and reconstructs possible histories of lesions. By combining the data from different patients, PITCH is able to capture statistically robust historical relationships between driver alterations in tumors and to represent these relationships as an evolutionary network. We will calibrate the approach in a longitudinal cohort of nearly 1,500 Chronic Lymphocytic Leukemia patients spanning a period of 12 years, along with 20 Glioblastoma Multiforme samples. We aim to extend and thus experimentally validate the approach using the large collection of Glioblastoma Multiforme and Low Grade Glioma in The Cancer Genome Atlas. We will be able to provide a robust computational approach that can be easily extended to any other tumor type where large cross-sectional data is available. Discovery of alterations associated to different phases, stages or therapeutic strategies could provide invaluable biomarkers for personalized approaches based on genomic data.

描述（由申请人提供）：癌症是通过基因组改变的积累进行的动态过程。大型测序项目阐明了许多肿瘤的复杂静态景观。但是，这些研究未能解决癌症的动态性质。了解肿瘤是如何通过选择性压力塑造的，对疗法和预后产生了影响。为了解决这个问题，我们提出了音调（肿瘤克隆异质性的简约推理），该计算模型旨在使用横截面研究中的高吞吐量基因组数据来揭示肿瘤的进化史。俯仰可以识别旧克隆的痕迹，并重建可能的病变病史。通过将来自不同患者的数据结合在一起，PITD能够捕获肿瘤驾驶员改变之间的统计牢固历史关系，并将这些关系表示为进化网络。我们将在近1,500名慢性淋巴细胞性白血病患者的纵向队列中校准该方法，以及20年的胶质母细胞瘤多形样品。我们旨在使用大量癌症基因组图集中大量的多形和低级胶质瘤的胶质母细胞瘤和低级神经胶质瘤进行实验验证该方法。我们将能够提供强大的计算方法，该方法可以轻松地扩展到可用的大型横截面数据的任何其他肿瘤类型。发现与不同阶段，阶段或治疗策略相关的变化可以根据基于基因组数据为个性化方法提供宝贵的生物标志物。