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

描述（由申请人提供）：癌症是一个动态过程，通过基因组改变的积累而进行。大型测序项目已经阐明了大量肿瘤中复杂的静态变化景观。然而，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形成的，对治疗和诊断具有重要意义。为了解决这个问题，我们提出了PITCH（肿瘤克隆异质结的简约推理），一个计算模型，旨在揭示肿瘤的进化历史，使用高通量的基因组数据从横断面研究。PITCH识别了较老克隆的痕迹，并重建了可能的病变历史。通过结合来自不同患者的数据，PITCH能够捕获肿瘤中驱动改变之间的统计学上稳健的历史关系，并将这些关系表示为进化网络。我们将在一个纵向队列中校准该方法，该队列包括近1,500名慢性淋巴细胞白血病患者，时间跨度为12年，沿着20份多形性胶质母细胞瘤样本。我们的目标是扩展，从而实验验证的方法，使用大量收集的多形性胶质母细胞瘤和低级别胶质瘤的癌症基因组图谱。我们将能够提供一个强大的计算方法，可以很容易地扩展到任何其他肿瘤类型的大截面数据是可用的。发现与不同阶段、阶段或治疗策略相关的改变可以为基于基因组数据的个性化方法提供宝贵的生物标志物。

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.