Single cell quantification of genomic instability in cancer as a determinant of therapeutic response

项目摘要 肿瘤遗传异质性是癌症生物学的广泛特征，并且是患者对肿瘤治疗的反应的基础。 疗法肿瘤异质性的一个方面一直难以研究，这是大基因组异质性。 畸变，包括几兆碱基的高水平扩增，整个或部分染色体增益 以及整个基因组的复制。这是因为在亚克隆中鉴定这些畸变 当对“批量”肿瘤进行测序时，细胞群（存在于<100%的细胞中）是极其具有挑战性的。单细胞 然而，基因组学可以在细胞分辨率下解决这些改变，从而能够精确定量 在这些基因组长度尺度上的异质性。全面调查的程度和后果， 由这些类型的基因组畸变产生的肿瘤内异质性，我将利用最近的进展， 强大的高度可扩展的单细胞全基因组测序和我在计算建模方面的专业知识。在 在K99阶段的奖项，我将研究如何在细胞修复其基因组的能力的差异， 导致不同的遗传异质性模式，以及这种细胞多样性如何导致差异 高级别浆液性卵巢癌的治疗反应，这是一种由基因组不稳定性驱动的癌症。在 该奖项的独立阶段，我将专注于异质性和进化动力学的染色体外 DNA，小的环状DNA片段，导致癌基因的高水平扩增。的结果 该提案有可能为基因组不稳定性的生物学提供基本的新见解， 更好地预测患者对治疗的反应，并识别可能 被剥削这份提案还描述了一个培训计划，以促进我的职业生涯，以一个独立的调查员， 结合受进化理论启发的计算建模，机器学习和高分辨率 基因组学来量化癌症演变，以便更好地预测患者对治疗的反应，并揭示 推动癌症进展的机制。在K99阶段，我将得到跨学科团队的支持 单细胞基因组学、癌症进化、卵巢癌生物学和基因组不稳定性方面的专家。我会 拓宽了我在机器学习、基因组不稳定性和可扩展生物信息学软件方面的知识 工程和提高我的沟通和领导能力对我的过渡至关重要。

PROJECT ABSTRACT Tumor genetic heterogeneity is an extensive feature of cancer biology and underlies patient response to therapy. One aspect of tumor heterogeneity that has been difficult to study is heterogeneity of large genomic aberrations, including high level amplifications a few megabases in size, whole or partial chromosomal gains and losses and whole genome duplications. This is because identifying these aberrations in subclonal populations (present in <100% of cells) is extremely challenging when sequencing tumors in “bulk”. Single cell genomics however, can resolve these alterations at cellular resolution enabling precise quantification of heterogeneity at these genomic length scales. To comprehensively investigate the extent and consequences of intra-tumor heterogeneity generated by these types of genomic aberrations I will leverage recent advances in robust highly scalable single cell whole genome sequencing and my expertise in computational modeling. In the K99 phase of the award I will investigate how differences in the ability of cells to repair their genomes results in different patterns of genetic heterogeneity, and how such cellular diversity can cause differential response to treatment in high grade serous ovarian cancer, a cancer driven by genomic instability. In the independent phase of the award I will focus on heterogeneity and evolutionary dynamics of extra-chromosomal DNA, small circular pieces of DNA that cause high level amplification of oncogenes. The results of this proposal have the potential to give fundamental new insight into the biology of genomic instability and enable better predication of patient response to therapy and identification of therapeutic vulnerability that may be exploited. This proposal also describes a training plan to advance my career to an independent investigator, combining computational modeling inspired by evolutionary theory, machine learning and high-resolution genomics to quantify cancer evolution in order to better predict patient response to therapy and uncover the mechanisms driving cancer progression. During the K99 phase I will be supported by an interdisciplinary team of experts in single cell genomics, cancer evolution, ovarian cancer biology and genomic instability. I will broaden my knowledge of machine learning, genomic instability and scalable bioinformatics software engineering and improve my communication and leadership skills vital for my transition.