Area C: Genome-wide identification and targeting of resistance to cancer therapy

C 区：全基因组鉴定和针对癌症治疗耐药性的靶向

• 批准号: 9482962
• 负责人: Jorge Silvio Gutkind
• 金额: $ 21.62万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2018-04-04
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9482962
• 关键词: Address; Aftercare; Algorithms; Antineoplastic Agents; Area; Cancer cell line; Cells; Characteristics; Clinical Research; Clustered Regularly Interspaced Short Palindromic Repeats; Collection; Colon Carcinoma; Combined Modality Therapy; Communities; DNA sequencing; Data; Data Set; Development; Drug Combinations; Drug resistance; Event; Evolution; Gene Silencing; Genes; Genetic; Genomics; Head Cancer; Head and neck structure; Individual; Lead; Letters; Malignant Neoplasms; Measures; Methylation; Molecular; Molecular Profiling; Monitor; Mutation; Neck Cancer; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Publishing; Recommendation; Research Design; Resistance; Sampling; Testing; The Cancer Genome Atlas; Therapeutic; Work; base; cancer cell; cancer genome; cancer therapy; cancer type; cohort; combinatorial; data mining; experimental study; fitness; gene interaction; genome-wide; genome-wide analysis; high throughput screening; interest; malignant breast neoplasm; melanoma; novel; response; screening; small molecule; success; targeted treatment; tool; transcriptome sequencing; tumor; user-friendly

Summary: Area C: Genome-wide identification and targeting of resistance to cancer therapy The frequent emergence of resistance to anti-cancer therapies remains a major challenge in cancer treatment that is of utmost importance. Recent clinical and experimental studies addressing this problem require the arduous collection of pre- and post- treatment data for every new specific treatment and cancer type studied. Thus, a computational approach that can expedite the identification of molecular determinants of resistance via the analysis of existing large-scale cancer cohorts is called for. Our proposal seeks to identify novel ways to counter de novo and acquired resistance through drug combinations. We focus on gene interactions rather than individual genes and leverage large scale genomic datasets and patient response data. Our approach is based on recent work in the Ruppin and other labs showing that genetic interactions can be computationally identified by analyzing omics tumor data. To decipher pathways of resistance to cancer therapies, we focus here on studying a new type of genetic interactions, termed synthetic rescues (SRs). SRs denote a functional interaction between two genes whereby a fitness reducing change in the activity of one of the two genes (termed the vulnerable gene) is compensated by altered activity of another gene (termed the rescuer gene), which restores cell fitness and rescues it. We have recently developed tools for SR data-driven identification from large cancer tumors cohorts, successfully enabling the prediction of drug response and emergence of resistance in patients. Building on these transformational results the specific aims of this proposal are: Specific Aim 1. Perform a pan-cancer and cancer type-specific SR-based analyses, focusing on melanoma, breast, head and neck, and colon cancer, identifying the major rescuer genes in each cancer type, together with specific recommendations of combinatorial therapies mitigating resistance. Specific Aim 2. Develop a new version of SR analysis tools that will be made publically available for use by others in a standard, user friendly manner and includes analysis of gene methylation and genome wide mutation data, on top of other omics data already utilized in our previous SR inference tools. Specific Aim 3. Experimentally test predicted rescuer targets and combination therapies in patient derived resistant cancer cells. Taken together the proposed study, will present a transformative SR based approach for identifying and targeting resistance pathways across the whole cancer genome.

摘要：区域C：全基因组识别和抗癌治疗的靶向 抗癌药物耐药性的频繁出现仍然是癌症治疗中的一大挑战 这是最重要的。最近针对这一问题的临床和实验研究需要 为所研究的每一种新的特定治疗和癌症类型收集治疗前和治疗后的数据。 因此，一种可以加快鉴定抗性分子决定因素的计算方法 通过对现有大规模癌症队列的分析，提出了建立癌症队列的建议。 我们的建议旨在找出通过药物对抗从头开始和获得性耐药性的新方法 组合。我们关注的是基因间的相互作用，而不是单个基因，并利用大规模基因组 数据集和患者响应数据。我们的方法是基于Ruppin和其他实验室的最新工作 表明可以通过分析组学肿瘤数据来计算识别遗传交互作用。 为了破译癌症治疗的耐药途径，我们在这里重点研究了一种新型的基因 相互作用，称为合成救援(SRS)。SRS表示两个基因之间的功能相互作用，其中 适合度降低两个基因之一(称为脆弱基因)活性的变化通过以下方式补偿 另一种基因(称为救助者基因)的活性发生了变化，它可以恢复细胞的健康并拯救它。我们有 最近开发的工具，用于从大型癌症肿瘤队列中进行SR数据驱动的识别，成功 从而能够预测患者的药物反应和出现耐药性。建立在这些基础上 转型成果这项提议的具体目标是： 具体目标1.执行基于泛癌症和癌症类型特定SR的分析，重点是 黑色素瘤、乳腺癌、头颈部和结肠癌，确定每种疾病的主要救治基因 癌症类型，以及减轻耐药性的联合治疗的具体建议。 具体目标2.开发新版本的SR分析工具，该工具将公开提供 供其他人以标准、用户友好的方式使用，并包括对基因甲基化和 基因组范围的突变数据，在我们之前的SR推理工具中已经使用的其他组学数据的基础上。 具体目标3.实验测试预测的救援目标和联合疗法 患者衍生的耐药癌细胞。 综上所述，拟议的研究将提出一种基于SR的变革性方法，用于识别和 靶向整个癌症基因组中的耐药途径。