Genomic prediction tools developed using phenotypes from disease progression models

使用疾病进展模型的表型开发的基因组预测工具

• 批准号: 9059138
• 负责人: Manish Sharma
• 金额: $ 18.95万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9059138
• 关键词: Accounting; Adverse event; Alternative Therapies; Area; Asia; Award; BAY 54-9085; Baltimore; Bioinformatics; Caliber; Cancer Patient; Characteristics; Chicago; Clinic; Clinical Data; Clinical Pharmacology; Clinical Trials; Collaborations; Collection; Community Clinical Oncology Program; Complex; Conduct Clinical Trials; Country; Course Content; DNA; Data; Data Element; Development; Development Plans; Disease; Disease Progression; Drug toxicity; Europe; European; Event; Fellowship; Future; Genetic; Genetic Variation; Genetic study; Genome Scan; Genomics; Genotype; Goals; Health; Hematology; Heritability; Image; Individual; Joints; K-Series Research Career Programs; Lead; Learning; Life; Malignant Neoplasms; Maryland; Measurement; Measures; Mentored Patient-Oriented Research Career Development Award; Mentors; Metastatic Renal Cell Cancer; Methods; Mining; Modeling; North America; Oncologist; Outcome; Patients; Pharmaceutical Preparations; Pharmacogenomics; Pharmacologic Substance; Phenotype; Physicians; Population; Predictive Factor; Predictive Value; Prognostic Factor; Progression-Free Survivals; Randomized; Renal Cell Carcinoma; Research; Research Personnel; SECTM1 gene; Science; Therapeutic; Time; Training; Translating; Treatment Efficacy; Tumor Burden; Tumor Volume; Tyrosine Kinase Inhibitor; Universities; Variant; Work; Writing; X-Ray Computed Tomography; base; cancer therapy; career; career development; clinical decision-making; drug efficacy; experience; genetic information; genetic variant; genome-wide; genomic data; improved; individual patient; interest; interpatient variability; meetings; novel; novel therapeutics; oncology; personalized medicine; phase III trial; programs; prospective; rare variant; response; skills; tool; treatment effect; treatment planning; tumor; whole genome

 DESCRIPTION (provided by applicant): My prior training and long-term career goals make this Translational Scholar Award in Pharmacogenomics and Personalized Medicine (K23) the ideal opportunity to help me become an independent investigator. Within the last three years, I completed joint fellowships in Hematology/Oncology and Clinical Pharmacology and Pharmacogenomics at the University of Chicago. My fellowship research introduced me to the field of pharmacometrics, which is the science of quantifying drug, disease and trial characteristics. Specifically, I became interested i using pharmacometrics to guide the development and optimize the use of cancer therapeutics. I furthered this interest as a Paul Calabresi K12 scholar over the last two years, during which I earned a Master's in Pharmacometrics from one of the only programs in pharmacometrics in this country at the University of Maryland - Baltimore. At the same time, I have gained significant experience writing and conducting clinical trials with major pharmacogenomic components. To advance my career as an independent investigator, I now ask whether pharmacometrics can improve the ability to translate genomic data into clinical decision-making. There have been many pharmacogenomic discoveries describing associations between germline genetic variation and drug toxicity or efficacy. Further discoveries regarding drug efficacy may be enhanced in two ways: first, by identifying better phenotypes of drug efficacy; and second, by utilizing large numbers of common variants in prediction tools rather than relying on a small number of variants. In my fellowship research, we developed a disease progression model of renal cell carcinoma (RCC) that can estimate the treatment effect of drug in the population and in individual patients. This model-estimated treatment effect is an intriguing potential phenotype, as it takes into consideration the full set of longitudinal data regarding tumor size, in contrast to more conventional phenotypes such as objective response (decrease in tumor size by ≥ 30%), progression-free survival and overall survival. Collaborators have shown that semi-automated measurements of tumor volumes, in contrast to the longest diameters on cross-sectional images, might further enhance these phenotypes by providing a more precise assessment of tumor burden. Finally, colleagues of mine at the University of Chicago have developed a method (called OmicKriging) for using all common variants identified during whole genome interrogation (and potentially other -omic data) to make predictions about a phenotype. The COMPARZ trial was the largest ever conducted randomized phase III trial in metastatic RCC, with 1,110 patients randomized to pazopanib versus sunitinib in North America, Europe, and Asia. Approximately two-thirds of these patients provided germline DNA for genome-wide genotyping, which has already been completed. As part of an ongoing collaboration with GlaxoSmithKline Pharmaceuticals (GSK), who sponsored the trial, we have access to clinical data, images from computed tomography scans, and genome-wide genotyping data for these patients. These data offer a unique opportunity to revise our previous disease progression model of RCC using two new therapies and a new phenotype (longitudinal tumor volume), and to explore how common variants can be used to predict both model-estimated treatment effect and conventional phenotypes such as objective response, PFS and OS. The hypothesis is that phenotypes estimated by disease progression models will lead to better genomic prediction tools than conventional phenotypes. These tools could predict which patients are more or less likely to benefit from therapy with tyrosine kinase inhibitors in metastatic RCC. Additionally, these tools could be improved by adding other data elements (such as tumor genotype) and could serve as a blueprint for similar tools using model-based phenotypes in other cancers and other complex diseases. In the research plan, I describe four steps (aims) that logically take us from the raw data to validated genomic prediction tools for both model-based and conventional phenotypes. The first step is to capture the phenotype of model-estimated treatment effect for each patient by measuring tumor volumes and revising our previous disease progression model of RCC. The second step is to estimate the heritability of this phenotype and the conventional ones, in order to understand the "upper limit" of interpatient variability that might be accounted for by genomic data. The third step is to develop the genomic prediction tools using the OmicKriging approach, and the fourth (and final) step is to validate these tools in a prospective clinical trial. In order to be successful in developing genomic prediction tools and prospectively validating them in clinical trials with cancer therapeutics, I need additional training and experience in the areas of genomics and statistical genetics. I have identified two mentors with expertise in these areas to oversee my career development plan. With their guidance, I have planned a comprehensive curriculum of courses and meetings for advanced training in genomics and statistical genetics to supplement my advanced degree in pharmacometrics. With this K23 award, I will acquire the skills to independently develop genomic prediction tools with phenotypes derived from disease progression models and prospectively validate these tools in clinical trials. In future work, I wil demonstrate how these tools can be used to personalize the treatment plan for cancer patients and ultimately improve their outcomes.

 描述（由申请人提供）： 我之前的培训和长期职业目标使药物基因组学和个性化医学转化学者奖 (K23) 成为帮助我成为一名独立研究者的理想机会。在过去的三年里，我完成了血液学/肿瘤学和临床药理学的联合奖学金。我的奖学金研究使我进入了药物计量学领域，这是一门量化药物、疾病和试验特征的科学，具体来说，我对使用药物计量学来指导产生了兴趣。在过去的两年里，作为 Paul Calabresi K12 学者，我进一步加深了对癌症疗法的开发和优化使用的兴趣，在此期间，我从该国唯一的药物计量学项目之一获得了药物计量学硕士学位。 同时，我在马里兰大学巴尔的摩分校获得了丰富的写作和写作经验。 为了推进我作为独立研究者的职业生涯，我现在想知道药理学是否可以提高将基因组数据转化为临床决策的能力。 已经有许多药物基因组学发现描述了种系遗传变异与药物毒性或功效之间的关联，可以通过两种方式增强有关药物功效的进一步发现：第一，通过识别更好的药物功效表型；第二，通过利用大量常见变异。在我的奖学金研究中，我们开发了一种肾细胞癌（RCC）疾病进展模型，可以估计药物在人群和个体患者中的治疗效果。估计的治疗效果是一个有趣的潜力表型，因为它考虑了有关肿瘤大小的全套纵向数据，与更传统的表型相比，例如客观缓解（肿瘤大小减少≥30％）、无进展生存期和总生存期。与横截面图像上的最长直径相比，半自动测量肿瘤体积可能会通过提供更精确的肿瘤负荷评估来进一步增强这些表型。最后，我在芝加哥大学的同事开发了一种方法（称为奥米克里金法）使用全基因组询问期间识别的所有常见变异（以及可能的其他组学数据）来预测表型。 COMPARZ 试验是迄今为止规模最大的转移性肾细胞癌随机 III 期试验，在北美、欧洲和亚洲有 1,110 名患者随机接受帕唑帕尼与舒尼替尼治疗，其中约三分之二的患者提供了用于全基因组基因分型的种系 DNA。作为与赞助该试验的葛兰素史克制药公司 (GSK) 持续合作的一部分，我们可以获得临床数据、计算图像。这些患者的断层扫描和全基因组基因分型数据提供了一个独特的机会，可以使用两种新疗法和新表型（纵向肿瘤体积）来修改我们之前的 RCC 疾病进展模型，并探索常见变异的发生方式。用于预测模型估计的治疗效果和常规表型，例如客观反应、PFS 和 OS。假设通过疾病进展模型估计的表型将产生比传统表型更好的基因组预测工具。此外，这些工具可以通过添加其他数据元素（例如肿瘤基因型）来改进，并且可以作为在其他癌症中使用基于模型的表型的类似工具的蓝图。以及其他复杂的疾病。 在研究计划中，我描述了四个步骤（目标），从逻辑上将我们从原始数据带到基于模型和传统表型的经过验证的基因组预测工具第一步是捕获每个模型估计的治疗效果的表型。通过测量肿瘤体积并修改我们之前的 RCC 疾病进展模型来对患者进行第二步是估计该表型和传统表型的遗传力，以了解可能由基因组解释的患者间变异性的“上限”。第三个数据。第一步是使用 OmicKriging 方法开发基因组预测工具，第四步（也是最后一步）是在前瞻性临床试验中验证这些工具。 为了成功开发基因组预测工具并前瞻性地验证它们 癌症治疗的临床试验，我需要以下领域的额外培训和经验 我已经找到了两位在这些领域具有专业知识的导师来监督我的职业发展计划，在他们的指导下，我计划了一套全面的基因组学和统计遗传学高级培训课程和会议，以补充我的高级学位。凭借这个 K23 奖项，我将获得独立开发具有疾病进展模型表型的基因组预测工具的技能，并在临床试验中前瞻性地验证这些工具，我将演示如何使用这些工具来个性化治疗。癌症治疗计划患者并最终改善他们的治疗结果。