Novel Algorithms for Detecting the Clonal Composition and Monitoring the Evolution of Tumours via Liquid Biopsies Sequencing Data

通过液体活检测序数据检测克隆组成和监测肿瘤进化的新算法

• 批准号: RGPIN-2017-05952
• 负责人: Hach, Faraz
• 金额: $ 1.89万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711000
• 关键词: Novel; Algorithms; Detecting; Clonal; Composition

Background. Cancer is a disease of the genome - more specifically mutations acquired by the genomic DNA in the cells of a human over a lifetime, which disrupt the cells' key functions and make them grow uncontrollably. DNA sequencing projects have led us to discover that cancer calls involve many genetic changes and that even in a single tumour, there are often multiple cancer cell populations that each carry their own mutations. Understanding this collection of mutations is important because we need to select therapies that kill all of the cancer cells, not just some of them. Unfortunately, existing computer programs for analyzing "normal" human genome data generated by genome sequencing technologies are limited in scope because they cannot fully characterize all the mutations present in the individual cells of a tumour tissue. Ideally, researchers would like to monitor how the genomes of cancer cells mutate over time, forming distinct tumour clones within the same tissue, and how cancer cells travel through the blood stream or the urinary tract and colonize other tissues, forming metastatic tumours. The new liquid biopsy technology has made it possible to capture tumour DNA circulating in the blood stream and to sequence it, however analyzing such data and identifying and cataloguing the spectrum of mutations in an individual patient will require new mathematical and computational approaches. Research Project. We propose to develop novel computational methods for (i) detecting SNVs, indels, microSVs, gene fusions and CNVs specific to a tumour (Objective 1), (ii) detecting the clonal composition of the tumour based on this mutational profile (Objective 2), and (iii) monitoring the tumour's progression during treatment (Objective 3) - via a composition of solid tumour WGSS and liquid biopsies sequencing provided as time series data. The short term goal is to develop mathematical models, computational algorithms and software (Objective 1-3) in order to analyze the next generation sequencing data obtained from “liquid biopsy”. These methods will be used by life scientist to better understand the biology underlying tumour evolution. The long term goal is to build a comprehensive and simple to use platform (potentially based on machine learning approaches) capable of predicting patient's response to different treatments depending on its genomic profile. Deliverables. The computational pipeline CVEMCT (Clonal Variant calling and Evolutionary Modeling based on CTDNA) which includes algorithms and open source software for clonality detection and tumour progression monitoring. All these tools will be also made available through the CGC The Cancer Genome Collaboratory, a Genome Canada funded project to host the entire ICGC cancer genome data first for the Canadian and then for the International research community.

背景资料。癌症是一种基因组疾病-更具体地说，是人类细胞中基因组DNA在一生中获得的突变，这些突变扰乱了细胞的关键功能，使它们无法控制地生长。DNA测序项目让我们发现，癌症电话涉及许多基因变化，即使在一个肿瘤中，往往也有多个癌细胞群体，每个癌细胞群体都带有自己的突变。了解这一系列突变很重要，因为我们需要选择杀死所有癌细胞的治疗方法，而不仅仅是部分癌细胞。不幸的是，现有的用于分析由基因组测序技术产生的“正常”人类基因组数据的计算机程序范围有限，因为它们不能完全表征肿瘤组织单个细胞中存在的所有突变。理想的情况是，研究人员希望监测癌细胞的基因组如何随着时间的推移发生突变，在同一组织内形成不同的肿瘤克隆，以及癌细胞如何通过血流或尿路传播并在其他组织中定居，形成转移肿瘤。新的液体活组织检查技术使捕获血液中循环的肿瘤DNA并对其进行排序成为可能，然而，分析此类数据并识别和分类单个患者的突变谱将需要新的数学和计算方法。 研究项目。我们建议开发新的计算方法，用于(I)检测肿瘤特异的SNV、INDELS、MicroSVs、基因融合和CNV(目标1)，(Ii)基于该突变特征检测肿瘤的克隆成分(目标2)，以及(Iii)监测治疗期间的肿瘤进展(目标3)-通过作为时间序列数据提供的固体肿瘤WGSS和液体活检测序的组合。 短期目标是开发数学模型、计算算法和软件(目标1-3)，以便分析从“液体活组织检查”获得的下一代测序数据。这些方法将被生命科学家用来更好地理解肿瘤进化的生物学基础。长期目标是建立一个全面且简单易用的平台(可能基于机器学习方法)，能够根据患者的基因组图谱预测患者对不同治疗的反应。 可交付成果。计算流水线CVEMCT(基于ctDNA的克隆变体调用和进化建模)，包括克隆检测和肿瘤进展监测的算法和开源软件。所有这些工具也将通过CGC癌症基因组合作实验室提供，这是一个由基因组加拿大资助的项目，首先为加拿大人托管整个ICGC癌症基因组数据，然后为国际研究界提供。