Comprehensive and Robust Tools for Analysis of Tumor Heterogeneity and Evolution

用于分析肿瘤异质性和进化的全面而强大的工具

基本信息

批准号：
10700040
负责人：
Benjamin Raphael
金额：
$ 61.86万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-24 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10700040
关键词：
Algorithmic Software Algorithms Cancer Center Cancer Patient Cells Chromosomal Rearrangement Cloud Computing Collaborations Collection Complement Complex Computer software Computers DNA DNA Sequence DNA sequencing Data Data Analyses Data Analytics Development Diagnosis Documentation Dropout Environment Evolution Genome Genomics Heterogeneity Individual Libraries Malignant Neoplasms Mathematics Measurement Modeling Molecular Multiple Anatomic Sites Mutation Mutation Analysis Names Neoplasm Metastasis Normal Cell Nucleotides Output Patients Pattern Phylogenetic Analysis Phylogeny Primary Neoplasm Process Recording of previous events Recurrence Research Personnel Resistance Resolution Running Sampling Single Nucleotide Polymorphism Software Tools Somatic Mutation Statistical Algorithm Structure Technology Testing Time Training analytical tool cancer cell cancer diagnosis cancer therapy cluster computing cohort combinatorial computerized tools data format driver mutation epigenomics exome sequencing individual patient insight interactive tool neoplastic cell novel strategies open source precision medicine precision oncology reconstruction resistance mechanism sequencing platform single cell sequencing software development sound targeted sequencing tool transcriptome sequencing treatment response tumor tumor heterogeneity user friendly software user-friendly whole genome

项目摘要

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.

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希望策略。这些方法是挑战是肿瘤是随着时间和时间随着时间而变化的细胞的异质集合对治疗的反应。在DNA序列水平上，单核苷酸的变化范围的变化范围大型染色体重排和全基因组重复的突变。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。但是，分析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。我们建议开发一个综合软件工具包，分析跨空间，时间，和基因组量表。该工具包将基于高级组合和统计算法由PI在过去几年中开发。这些算法将被统一为强大的计算上有效，统计上有声音软件包。该工具包将合并模块对于不同类型的肿瘤样品，包括单个肿瘤样品，多个肿瘤区域，多个解剖部位（例如原发性肿瘤和转移）和多个时间点。该软件也将分析来自不同测序方法的数据（全基因组，全象征和目标测序）和不同的测序技术，包括散装肿瘤，单细胞，短阅读和长阅读。该软件包将是开源的，并将发布以在单个计算机上运行，计算群集或云计算环境中。广泛的文档和培训将是提供的旨在促进从专家生物信息学家到临床医生的广泛用户使用。这些强大的数据分析工具将使研究人员能够表征肿瘤中的异质性高精度，在癌症诊断和治疗方面具有更高的精度。