Comprehensive and Robust Tools for Analysis of Tumor Heterogeneity and Evolution

项目摘要/摘要 近年来，基于单个患者的分子变化的精确医学方法 肿瘤已成为诊断和治疗癌症的一种很有前途的策略。这些方法是 挑战的事实是，肿瘤是随着时间和时间的变化而变化的异质细胞集合 对治疗的反应。在DNA序列水平上，规模的变化范围从单核苷酸 从突变到大的染色体重排和全基因组复制。新的DNA/RNA 测序技术能够测量这种异质性，并提供数据来推断 肿瘤的进化史。然而，分析这些数据所需的算法和软件 肿瘤异质性和进化的复杂性在范围上仍然有限。我们建议开发一种 全面的软件工具包，可以分析肿瘤的异质性和跨空间、跨时间、 和基因组规模。该工具包将基于先进的组合和统计算法 由PI在过去几年中开发的。这些算法将统一为一个健壮的、 计算效率高，统计上合理的软件包。此工具包将包含模块 针对不同类型的肿瘤样本，包括单个肿瘤样本、多个肿瘤区域、多个 解剖部位(如原发肿瘤和转移)和多个时间点。该软件还将 分析来自不同测序方法的数据(全基因组、全外显子组和靶向测序 测序)和不同的测序技术，包括块状肿瘤、单细胞、短读和 长篇大论。该软件包将是开源的，并将发布以在个人计算机上运行， 计算集群，或在云计算环境中。将提供广泛的文档和培训 提供以便于从专业生物信息学家到临床医生的广泛用户的使用。这些 强大的数据分析工具将使研究人员能够表征肿瘤内的异质性 高精确度，使癌症诊断和治疗更精确。

Project Summary/Abstract In recent years, precision medicine approaches based on molecular changes in an individual patient’s tumor have become a promising strategy for diagnosis and treatment of cancer. These approaches are challenged by the fact that tumors are a heterogeneous collection of cells that change over time and in response to treatment. At the DNA sequence level, changes range in scale from single-nucleotide mutations to large chromosomal rearrangements and whole-genome duplications. New DNA/RNA sequencing technologies enable measurement of this heterogeneity and provide data to infer the evolutionary history of a tumor. However, the algorithms and software necessary to analyze the complexities of tumor heterogeneity and evolution remain limited in scope. We propose to develop a comprehensive software toolkit to analyze tumor heterogeneity and tumor evolution across space, time, and genomic scale. This toolkit will be based on advanced combinatorial and statistical algorithms developed by PI over the past several years. These algorithms will be unified into a robust, computationally efficient, and statistically sound software package. This toolkit will incorporate modules for different types of tumor samples including single tumor samples, multiple tumor regions, multiple anatomical sites (e.g. primary tumor and metastasis), and multiple time points. The software will also analyze data from different sequencing approaches (whole-genome, whole-exome, and targeted sequencing) and different sequencing technologies including bulk tumor, single-cell, short-read, and long-read. The software package will be open source and will be released to run on individual computers, computing clusters, or in cloud computing environments. Extensive documentation and training will be provided to facilitate use by a wide range of users from expert bioinformaticians to clinicians. These powerful data analytic tools will enable researchers to characterize the heterogeneity within tumors with high accuracy, enabling greater precision in cancer diagnosis and treatment.