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

癌症基因组不稳定性的单细胞定量作为治疗反应的决定因素

• 批准号: 10357908
• 负责人: Marc Williams
• 金额: $ 9.98万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-03 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10357908
• 关键词: Aftercare; Alleles; Automobile Driving; Award; BRCA1 gene; BRCA2 gene; Bioinformatics; Biological; Biology; Breast Epithelial Cells; CCNE1 gene; Cancer Biology; Cancer Patient; Cell Line; Cells; Chromosomal Gain; Chromosomal Loss; Collection; Communication; Computer Models; Copy Number Polymorphism; DNA; DNA Damage; DNA Repair; DNA Repair Pathway; Data; Defect; Elements; Environment; Evolution; Exhibits; Fluorescent in Situ Hybridization; Gene Expression; Genetic Heterogeneity; Genome; Genomic Instability; Genomics; Heterogeneity; Immersion; Knowledge; Leadership; Length; Lesion; Loss of Heterozygosity; Machine Learning; Maintenance; Malignant Neoplasms; Malignant neoplasm of ovary; Mentors; Methods; Modeling; Mutagenesis; Neoplasm Metastasis; Oncogenes; Pathway interactions; Pattern; Phase; Phenotype; Play; Population; Prediction of Response to Therapy; Primary Neoplasm; Process; Prognosis; Property; Relapse; Research; Research Personnel; Research Proposals; Resistance development; Resolution; Role; Route; Sampling; Serous; Software Engineering; TP53 gene; Techniques; Testing; Therapeutic; Training; Translating; Translational Research; Treatment outcome; cancer cell; cancer genome; cancer genomics; cancer type; career; chromosome missegregation; design; effective therapy; extrachromosomal DNA; fitness; genome sequencing; genomic aberrations; genotoxicity; improved; insight; mutant; novel strategies; patient derived xenograft model; patient response; profiles in patients; programs; repaired; response; single cell technology; single-cell RNA sequencing; skills; theories; tool; treatment comparison; treatment response; tumor; tumor heterogeneity; tumor progression; whole genome

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.

项目摘要 肿瘤遗传异质性是癌症生物学的一个广泛特征，也是患者对药物反应的基础 肿瘤异质性的一个难以研究的方面是大基因组的异质性。 畸变，包括几兆碱基大小的高水平扩增、全部或部分染色体增益 这是因为在亚克隆中识别这些畸变。 对“批量”肿瘤进行测序时，群体（存在于 <100% 的细胞中）极具挑战性。 然而，基因组学可以在细胞分辨率下解决这些变化，从而能够精确量化 全面研究这些基因组长度尺度的异质性的程度和后果。 我将利用这些类型的基因组畸变产生的肿瘤内异质性的最新进展 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。 K99阶段的奖项我将研究细胞修复基因组能力的差异 导致不同模式的遗传异质性，以及这种细胞多样性如何导致差异 对高级别浆液性卵巢癌治疗的反应，这是一种由基因组不稳定驱动的癌症。 独立阶段的奖项我将重点关注染色体外的异质性和进化动力学 DNA，小的圆形 DNA 片段，导致癌基因的高水平扩增。 该提案有可能为基因组不稳定性生物学提供根本性的新见解，并使 更好地预测患者对治疗的反应并识别可能存在的治疗脆弱性 该提案还描述了一项将我的职业提升为独立调查员的培训计划， 结合受进化理论、机器学习和高分辨率启发的计算模型 基因组学量化癌症进化，以便更好地预测患者对治疗的反应并揭示 在 K99 阶段，驱动癌症进展的机制将得到跨学科团队的支持。 我将由单细胞基因组学、癌症进化、卵巢癌生物学和基因组不稳定性方面的专家组成。 拓宽我对机器学习、基因组不稳定性和可扩展生物信息学软件的知识 设计并提高我的沟通和领导技能，这对我的过渡至关重要。

项目成果

Single-cell genomic variation induced by mutational processes in cancer.

癌症突变过程诱导的单细胞基因组变异。

• 发表时间: 2022-12
• 影响因子: 64.8
• 作者: Funnell, Tyler;O'Flanagan, Ciara H;Williams, Marc J;McPherson, Andrew;McKinney, Steven;Kabeer, Farhia;Lee, Hakwoo;Salehi, Sohrab;Vázquez;Shi, Hongyu;Leventhal, Emily;Masud, Tehmina;Eirew, Peter;Yap, Damian;Zhang, Allen W;Lim
• 通讯作者: Lim

Single-cell DNA replication dynamics in genomically unstable cancers.

基因组不稳定癌症中的单细胞 DNA 复制动态。

• 发表时间: 2023-09-23
• 作者: Weiner, Adam C;Williams, Marc J;Shi, Hongyu;Vázquez;Salehi, Sohrab;Rusk, Nicole;Aparicio, Samuel;Shah, Sohrab P;McPherson, Andrew
• 通讯作者: McPherson, Andrew