Comparison of molecular factors to drug activities

分子因素与药物活性的比较

• 批准号: 10926634
• 负责人: William Reinhold
• 金额: $ 12.43万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10926634
• 关键词: Affective; Alkylating Agents; Antineoplastic Agents; Biological; Bleomycin; CDC2 gene; Cancer Patient; Cell Line; Characteristics; Cladribine; Clinical Trials; Complex; Computational Technique; Computer software; Core-Binding Factor; DNA Damage; DNA Methylation; DNA Repair; DNA Repair Gene; DNA Synthesis Inhibitors; DNA biosynthesis; Data; Databases; Disease; Drug Compounding; Drug Targeting; EGFR gene; Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor; Epigenetic Process; Erlotinib; Event; FDA approved; Gene Dosage; Genes; Genetic; Genomics; Goals; Knowledge; Lead; Linear Regressions; MDM2 gene; Machine Learning; Malignant Neoplasms; Manuscripts; Mathematics; Methodology; MicroRNAs; Modification; Molecular; Molecular Profiling; Outcome; Output; PTEN gene; Pathway interactions; Pattern; Pharmaceutical Preparations; Pharmacology; Pharmacology Study; Protein Isoforms; RAD52 gene; Ras/Raf; Resources; Structure; Subgroup; System; TP53 gene; Techniques; Tissues; Topoisomerase; Topoisomerase-I Inhibitor; Transcript; Variant; Visualization; analytical method; cancer therapy; cell growth; data integration; drug action; genetic variant; infancy; inhibitor; leukemia; mathematical learning; mathematical methods; multiple datasets; mutational status; novel; pharmacologic; response; statistical learning; translational applications; web app

Cancer is a disease that emerges though genetic and epigenetic alterations that perturb molecular networks including cell growth, survival, and differentiation. To develop more targeted and efficacious cancer treatments, it is essential to situate and understand drug actions in this networked, systems-level context. For most anti-cancer drugs, only partial knowledge exists about their detailed mechanism of action. Even where targets have been defined, as with FDA-approved and in-clinical-trial drugs, broader off-target effects are often poorly understood. Compound activity and genomic profiling data over well-characterized cell line panels allows one to attempt computational prediction of molecular drug response determinants. However, these computational techniques exist in a continuum of complexity, and each has its assets and shortcomings. We have and will use a combination of approaches ranging from the simple to the complex for these purposes. We employ Pearson's or Spearman's, or Matthew's correlation-based approaches that can identify genomic features within cell line profiles that are significantly correlated with a compound's activity profile. This methodology has demonstrated the ability to recognize robustly correlated parameters. Pearson's correlation is employed in our CellMiner "Pattern comparison", "Cross correlation", and "Genetic variant versus drug visualization", and utilize our "Cell line signature" and "Genetic variant summation" outputs. Our CellMinerCDB web-application uses Pearson's correlation in Compare Patterns and the scatter plot outputs. It also provides multi-variant analysis using either linear regression or the LASSO machine learning approach. In addition, we use state-of-the-art mathematical techniques in our manuscripts to compare our large drug compound database to our extensive network of molecular factors. Included in these forms of analysis may be gene and microRNA transcript expression, gene copy number, gene sequence variation, transcript isoform status, and DNA methylation status. Pathway enrichment analysis for those identified molecular factors with significantly correlated molecular profiles may be applied. The selection of which analytical method to use to identify biologically-related events is not settled or simplistic. It is influenced by the biological question being asked, the level of biological knowledge available, the data types available, and the strengths, weaknesses, and applicability of each mathematical approach. It remains a field in its infancy. Among our previous successfully identified list of molecular-pharmacological associations are i) SLFN11 transcript expression for topoisomerase 1 and 2 inhibitors, alkylating agents, and DNA synthesis inhibitors (PMID: 22927417), ii) the identification of Ro5-3335 as a lead compound for Core Binding Factor leukemias (PMID: 22912405), iii) TP53 mutational status and the activity of the MDM2-TP53 interaction inhibitor nutlin iv) a multifactorial combination of ERBB1 and 2 expression and RAS-RAF-PTEN mutational status for the activity of erlotinib (PMID: 23856246), v) ATAD5 mutational status for the DNA-damaging drugs bleomycin, zorbamycin, and peplomycin (PMID: 25758781) vi) genetic variants for the DNA replication and repair gene MUS81 with the DNA synthesis inhibitor cladribine (PMID: 26048278), vii) genetic variants for the DNA damage repair gene RAD52 for the DNA damaging ifosfomide (PMID: 25032700), CDK1, 20 transcript isoforms for the CDK inhibitor palbociclib (PMID: 31113817) and 46 diverse drug's activities for which the drug target is the same game gene whose molecular modification is correlated in a significant fashion (PMID: 32652468).

癌症是一种通过遗传和表观遗传改变而出现的疾病，这些改变扰乱了包括细胞生长、存活和分化在内的分子网络。为了开发更有针对性和更有效的癌症治疗方法，必须在这个网络化、系统级的背景下定位和理解药物的作用。对于大多数抗癌药物，对其详细作用机制仅存在部分了解。即使目标已经确定，例如 FDA 批准的临床试验药物，人们对更广泛的脱靶效应也往往知之甚少。充分表征的细胞系组上的化合物活性和基因组分析数据允许人们尝试分子药物反应决定因素的计算预测。然而，这些计算技术存在连续的复杂性，并且每种技术都有其优点和缺点。为了这些目的，我们已经并将使用从简单到复杂的方法组合。我们采用 Pearson 或 Spearman 或 Matthew 的基于相关性的方法，可以识别细胞系谱中与化合物活性谱显着相关的基因组特征。该方法已证明能够识别鲁棒相关的参数。 Pearson 相关性用于我们的 CellMiner“模式比较”、“交叉相关”和“遗传变异与药物可视化”，并利用我们的“细胞系特征”和“遗传变异求和”输出。我们的 CellMinerCDB Web 应用程序在比较模式和散点图输出中使用 Pearson 相关性。它还使用线性回归或 LASSO 机器学习方法提供多变量分析。此外，我们在手稿中使用最先进的数学技术，将我们的大型药物化合物数据库与我们广泛的分子因子网络进行比较。这些形式的分析可能包括基因和 microRNA 转录本表达、基因拷贝数、基因序列变异、转录本亚型状态和 DNA 甲基化状态。可以对那些具有显着相关分子谱的已识别分子因子应用通路富集分析。选择哪种分析方法来识别生物学相关事件尚未确定或过于简单化。它受到所提出的生物学问题、可用的生物学知识水平、可用的数据类型以及每种数学方法的优点、缺点和适用性的影响。它仍然是一个处于起步阶段的领域。我们之前成功鉴定的分子药理学关联列表包括 i) 拓扑异构酶 1 和 2 抑制剂、烷化剂和 DNA 合成抑制剂的 SLFN11 转录物表达 (PMID: 22927417)，ii) Ro5-3335 鉴定为核心结合因子白血病的先导化合物 (PMID: 22912405)，iii) TP53 突变状态和 MDM2-TP53 相互作用抑制剂的活性 nutlin iv) ERBB1 和 2 表达以及 RAS-RAF-PTEN 突变状态对厄洛替尼活性的多因素组合 (PMID: 23856246)，v) DNA 损伤药物博莱霉素、佐巴霉素和培普霉素的 ATAD5 突变状态 (PMID: 25758781) vi) DNA 复制和修复基因 MUS81 与 DNA 合成抑制剂克拉屈滨 (PMID: 26048278) 的遗传变异，vii) DNA 损伤异环磷酰胺的 DNA 损伤修复基因 RAD52 的遗传变异 (PMID: 25032700)、CDK1、20 种转录亚型 CDK 抑制剂 palbociclib (PMID: 31113817) 和 46 种不同药物的活性，其药物靶标是相同的游戏基因，其分子修饰以显着方式相关 (PMID: 32652468)。

项目成果

Tyrosyl-DNA Phosphodiesterase 1 and Topoisomerase I Activities as Predictive Indicators for Glioblastoma Susceptibility to Genotoxic Agents.

酪氨酰 DNA 磷酸二酯酶 1 和拓扑异构酶 I 活性作为胶质母细胞瘤对基因毒性药物敏感性的预测指标。

• DOI: 10.3390/cancers11101416
• 发表时间: 2019
• 期刊: Cancers
• 影响因子: 5.2
• 作者: Wang,Wenjie;Rodriguez-Silva,Monica;AcandadelaRocha,ArletM;Wolf,AizikL;Lai,Yanhao;Liu,Yuan;Reinhold,WilliamC;Pommier,Yves;Chambers,JeremyW;Tse-Dinh,Yuk-Ching
• 通讯作者: Tse-Dinh,Yuk-Ching

Temozolomide in the Era of Precision Medicine.

• DOI: 10.1158/0008-5472.can-16-2983
• 发表时间: 2017-02-15
• 期刊: Cancer research
• 影响因子: 11.2
• 作者: Thomas A;Tanaka M;Trepel J;Reinhold WC;Rajapakse VN;Pommier Y
• 通讯作者: Pommier Y

Cytidine Deaminase Deficiency Reveals New Therapeutic Opportunities against Cancer.

• DOI: 10.1158/1078-0432.ccr-16-0626
• 发表时间: 2017-04-15
• 期刊: Clinical cancer research : an official journal of the American Association for Cancer Research
• 作者: Mameri H;Bièche I;Meseure D;Marangoni E;Buhagiar-Labarchède G;Nicolas A;Vacher S;Onclercq-Delic R;Rajapakse V;Varma S;Reinhold WC;Pommier Y;Amor-Guéret M
• 通讯作者: Amor-Guéret M

Epigenetic inactivation of the putative DNA/RNA helicase SLFN11 in human cancer confers resistance to platinum drugs.

• DOI: 10.18632/oncotarget.6413
• 发表时间: 2016-01-19
• 期刊: Oncotarget
• 作者: Nogales V;Reinhold WC;Varma S;Martinez-Cardus A;Moutinho C;Moran S;Heyn H;Sebio A;Barnadas A;Pommier Y;Esteller M
• 通讯作者: Esteller M