Using clinical data to identify FDA-approved drugs for cancer prevention and therapeutic repurposing

使用临床数据来确定 FDA 批准的用于癌症预防和治疗再利用的药物

• 批准号: 9324540
• 负责人: Rachel Dania Melamed
• 金额: $ 12.5万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9324540
• 关键词: Adverse effects; Adverse event; Antineoplastic Agents; Area; Automobile Driving; Biological Assay; Biometry; Cancer Etiology; Cancer cell line; Cancerous; Case Series; Cell Line; Cell physiology; Cessation of life; Chicago; Clinic; Clinical; Clinical Data; Combined Modality Therapy; Complex; Data; Data Collection; Databases; Development; Development Plans; Disease; Disease Outcome; Drug Combinations; Drug Exposure; Drug Prescriptions; Drug resistance; Drug usage; Environment; Epidemiology; Event; FDA approved; Finding by Cause; Future; Gene Expression; Genomics; Goals; Human; In Vitro; Incidence; Individual; Investments; Joints; Laboratories; Learning Skill; Libraries; Link; Machine Learning; Malignant Neoplasms; Measures; Medical History; Medicine; Mentors; Mentorship; Methodology; Methods; Mining; Modeling; Molecular; Mutation; Nature; Oncogenic; Onset of illness; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Population; Premalignant; Process; Provider; Research; Research Infrastructure; Resources; Science; Scientist; Self-control as a personality trait; Signal Transduction; Social Sciences; Structure; Therapeutic; Tissues; Universities; Work; base; cancer cell; cancer prevention; cancer therapy; cancer type; career development; clinical effect; clinical investigation; clinical predictors; clinically relevant; combinatorial; cost; data access; disease heterogeneity; disorder risk; experience; experimental study; follow-up; gene discovery; genomic data; health data; in vitro Assay; in vivo; innovation; insight; interest; life history; novel; novel therapeutics; personalized medicine; premature; prevent; response; skills; statistics; targeted treatment; training opportunity; translational medicine; tumor

Project Summary/Abstract Clinical data collection is accelerating rapidly, and in the future it will include both provider- and patient- generated data. Hidden within this mass of noisy observational data are clues as to factors influencing disease onset and outcome. Finding ways to exploit this trove of disease data can unlock a new perspective on disease processes. We can tackle disease both from the bottom-up, from experimental data generated in the laboratory, and from the top down, from clinical phenomena observed across human populations. A particularly impactful and prevalent disease is cancer. Each tumor harbors a unique combination of mutations driving a distinct set of oncogenic processes. Targeted therapies have been proposed to pinpoint these mutations, potentially requiring a vast array of therapeutic options. Cancer treatment often fails when drug resistance arises, another result of the complex combinatorial nature of tumor alterations. Combination therapies have been proposed as an approach to interfere with multiple disease signals simultaneously. However, identifying effective drug combinations, and the cancer types in which they are effective, is experimentally infeasible, leading to a push for computational solutions. In this proposal, we combine methods from social sciences and biostatistics to find the causal effect of a drug on cancer onset from observational clinical data. Both increased and decreased cancer rates in drug-takers are of equal interest, as they can inform us of disease processes and provide clinical impact. We are particularly interested in finding drug combinations that impact cancer. These combination effects are unlikely to have been detected, and our clinical data provides a unique resource for observing the effects of tens of thousands of drug combinations. We will pool the resulting causal drug effect estimates across the many cancers present in our data. To gain insight into the cellular processes underlying clinical effect, we will examine the impact of known cancer-causing drugs in vitro, using large public cell line assays. The accompanying goal is to provide Dr. Rachel Melamed with a career development experience to become an independent scientist. Her research will use observational health data to understand the genesis of cancer, prevent the disease, and discover new therapeutic options. This proposal takes advantage of the interdisciplinary strengths of the University of Chicago in computation, biostatistics, and medicine, as well as institutional resources in terms of data access and infrastructure. Dr. Melamed has assembled a team consisting of complementary mentors and collaborators with expertise in computation, statistics, translational medicine, personalized therapy, and cancer therapy. The career development plan focuses on enhancing her statistics and machine learning skills with structured coursework and mentorship, and gaining experience in biomedical applications via applied work and mentorship. This will provide Dr. Melamed with skills to model observational data and to integrate the results with experimental data.

项目总结/摘要 临床数据收集正在迅速加速，未来将包括提供者和患者。 生成的数据。隐藏在这些嘈杂的观测数据中的是影响疾病因素的线索 发作和结果。找到利用这一疾病数据宝库的方法可以开启对疾病的新视角 流程.我们可以自下而上，从实验数据中， 从实验室，从上到下，从人群中观察到的临床现象。一个特别 最具影响力和流行的疾病是癌症。每个肿瘤都有一个独特的突变组合， 一组独特的致癌过程。已经提出了靶向治疗来精确定位这些突变， 可能需要大量的治疗选择。癌症治疗往往失败时，耐药性 这是肿瘤改变的复杂组合性质的另一个结果。联合疗法具有 被提出作为同时干扰多个疾病信号的方法。然而，识别 有效的药物组合，以及它们有效的癌症类型，在实验上是不可行的， 从而推动了计算解决方案。 在这个建议中，我们结合了社会科学和生物统计学的联合收割机方法来寻找药物的因果效应 对癌症发病的影响。吸毒者的癌症发病率增加和减少 同样重要的是，它们可以告诉我们疾病的过程并提供临床影响。我们 尤其是对发现影响癌症的药物组合感兴趣。这些组合效应不太可能 我们的临床数据提供了一个独特的资源，用于观察数十种 成千上万的药物组合。我们将汇总多个研究中的因果药物效应估计值， 癌症在我们的数据中。为了深入了解临床效应背后的细胞过程，我们将 使用大型公共细胞系试验，检查已知致癌药物的体外影响。 伴随的目标是为Rachel Melamed博士提供职业发展经验， 独立的科学家。她的研究将使用观察性健康数据来了解癌症的起源， 预防疾病，并发现新的治疗选择。该提案利用了 芝加哥大学在计算、生物统计和医学方面的跨学科优势，以及 在数据访问和基础设施方面的机构资源。梅拉梅德博士召集了一个小组 由互补的导师和合作者组成，他们在计算、统计、翻译、 药物、个性化治疗和癌症治疗。职业发展计划的重点是提高她的 通过结构化的课程和指导，掌握统计和机器学习技能，并获得以下方面的经验： 通过应用工作和导师制应用生物医学。这将为梅拉梅德博士提供建模技能， 观测数据，并将结果与实验数据相结合。