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
基本信息
- 批准号:RGPIN-2017-05952
- 负责人:
- 金额:$ 3.79万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2022
- 资助国家:加拿大
- 起止时间:2022-01-01 至 2023-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
背景癌症是一种基因组疾病-更具体地说,人类细胞中的基因组DNA在一生中发生突变,破坏了细胞的关键功能,使它们无法控制地生长。DNA测序项目使我们发现,癌症涉及许多遗传变化,即使在一个肿瘤中,通常也有多个癌细胞群,每个癌细胞群都携带自己的突变。了解这些突变的集合是很重要的,因为我们需要选择杀死所有癌细胞的疗法,而不仅仅是其中的一些。不幸的是,用于分析由基因组测序技术产生的“正常”人类基因组数据的现有计算机程序在范围上是有限的,因为它们不能完全表征肿瘤组织的个体细胞中存在的所有突变。理想情况下,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,在同一组织中形成不同的肿瘤克隆,以及癌细胞如何通过血流或尿路并定植在其他组织中,形成转移性肿瘤。新的液体活检技术使得捕获血流中循环的肿瘤DNA并对其进行测序成为可能,然而分析这些数据并识别和编目个体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)检测肿瘤特异性的SNV、indel、microSV、基因融合和CNV(目的1),(ii)基于该突变谱检测肿瘤的克隆组成(目标2),和(iii)在治疗期间监测肿瘤的进展(目标3)-通过作为时间序列数据提供的实体肿瘤WGSS和液体活检测序的组合物。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“液体活检”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了解肿瘤演变的生物学基础。长期目标是建立一个全面且易于使用的平台(可能基于机器学习方法),能够根据其基因组特征预测患者对不同治疗的反应。计算管道CVEMCT(基于CTDNA的克隆变异调用和进化建模),包括用于克隆性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,这是加拿大基因组资助的一个项目,首先为加拿大人,然后为国际研究界提供整个ICGC癌症基因组数据。
项目成果
期刊论文数量(0)
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Hach, Faraz其他文献
Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA
- DOI:
10.1093/nar/gkz067 - 发表时间:
2019-04-23 - 期刊:
- 影响因子:14.9
- 作者:
Gawronskii, Alexander R.;Lin, Yen-Yi;Hach, Faraz - 通讯作者:
Hach, Faraz
Fast and accurate matching of cellular barcodes across short-reads and long-reads of single-cell RNA-seq experiments.
- DOI:
10.1016/j.isci.2022.104530 - 发表时间:
2022-07-15 - 期刊:
- 影响因子:5.8
- 作者:
Ebrahimi, Ghazal;Orabi, Baraa;Robinson, Meghan;Chauve, Cedric;Flannigan, Ryan;Hach, Faraz - 通讯作者:
Hach, Faraz
CircMiner: accurate and rapid detection of circular RNA through splice-aware pseudo-alignment scheme
- DOI:
10.1093/bioinformatics/btaa232 - 发表时间:
2020-06-15 - 期刊:
- 影响因子:5.8
- 作者:
Asghari, Hossein;Lin, Yen-Yi;Hach, Faraz - 通讯作者:
Hach, Faraz
SCALCE: boosting sequence compression algorithms using locally consistent encoding
- DOI:
10.1093/bioinformatics/bts593 - 发表时间:
2012-12-01 - 期刊:
- 影响因子:5.8
- 作者:
Hach, Faraz;Numanagic, Ibrahim;Sahinalp, S. Cenk - 通讯作者:
Sahinalp, S. Cenk
Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation.
- DOI:
10.1038/s41588-022-01168-y - 发表时间:
2022-09 - 期刊:
- 影响因子:30.8
- 作者:
Baca, Sylvan C.;Singler, Cassandra;Zacharia, Soumya;Seo, Ji-Heui;Morova, Tunc;Hach, Faraz;Ding, Yi;Schwarz, Tommer;Huang, Chia-Chi Flora;Anderson, Jacob;Fay, Andre P.;Kalita, Cynthia;Groha, Stefan;Pomerantz, Mark M.;Wang, Victoria;Linder, Simon;Sweeney, Christopher J.;Zwart, Wilbert;Lack, Nathan A.;Pasaniuc, Bogdan;Takeda, David Y.;Gusev, Alexander;Freedman, Matthew L. - 通讯作者:
Freedman, Matthew L.
Hach, Faraz的其他文献
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{{ truncateString('Hach, Faraz', 18)}}的其他基金
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 - 财政年份:2021
- 资助金额:
$ 3.79万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2020
- 资助金额:
$ 3.79万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2019
- 资助金额:
$ 3.79万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2018
- 资助金额:
$ 3.79万 - 项目类别:
Discovery Grants Program - Individual
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 - 财政年份:2017
- 资助金额:
$ 3.79万 - 项目类别:
Discovery Grants Program - Individual
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