INFORMATICS METHODS AND MODELS FOR COMPUTATIONAL PATHOLOGY

计算病理学的信息学方法和模型

• 批准号: 8674406
• 负责人: Andrew H Beck
• 金额: $ 17.06万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-05 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8674406
• 关键词: Algorithms; Area; Bioinformatics; Biological Markers; Biomedical Computing; Biometry; Brain; Breast; Cancer Biology; Cancer Patient; Clinical; Clinical Data; Clinical Treatment; Clinical Trials; Collection; Communities; Complex; Computational Biology; Computer Simulation; Computing Methodologies; Dana-Farber Cancer Institute; Data; Development; Diagnostic; Disease; Future; Gene Expression; Gene Expression Alteration; Gene Expression Profile; Genomics; Goals; Heterogeneity; Hospitals; Human; Image; Image Analysis; Informatics; Israel; K22 Award; Kidney; Lead; Link; Lung; Machine Learning; Malignant Neoplasms; Malignant neoplasm of brain; Malignant neoplasm of lung; Measures; Medical center; Medicine; Mentors; Methods; Microscopic; Modeling; Molecular; Molecular Profiling; Ontology; Outcome; Pathologist; Pathology; Patients; Pediatric Hospitals; Pediatrics; Phenotype; Procedures; Radiology Specialty; Renal carcinoma; Research; Sampling; Science; System; Testing; The Cancer Genome Atlas; Therapeutic; Tissue Microarray; Translating; Validation; Vertebral column; Woman; base; biomedical informatics; cancer cell; cancer gene expression; cancer type; career; clinical care; design; disease phenotype; improved; insight; knowledge base; malignant breast neoplasm; medical schools; multitask; novel; predictive modeling; professor; prognostic; programs; transcriptomics; translational medicine

Abstract The applicant is an Assistant Professor at Beth Israel Deaconess Medical Center and Harvard Medical School. The applicant's research program focuses on developing methods in translational bioinformatics to integrate heterogeneous biomedical data types (genomic, transcriptomic, microscopic, clinical) enabling the development of improved diagnostics and therapeutics for patients. Recent projects completed by the applicant include: the development of the Computational Pathology (C-Path) platform for building prognostic models from microscopic image data (Science Translational Medicine, 2011); and the development of the Significance Analysis of Prognostic Signatures method for identifying robust prognostic gene expression signatures form clinically annotated genomic data (PLoS Computational Biology, 2013). This K22 award will provide the applicant with the support necessary to accomplish the following goals: (1) to become an expert at designing and using ontologies to model Computational Pathology data and to support a Computational Pathology Knowledgebase; (2) to become an expert at integrative predictive modeling of Computational Pathology data, gene expression data, and clinical outcomes data; and (3) to develop and manage an independent research career in translational bioinformatics. To achieve these goals, the applicant has assembled a team of mentors and collaborators with expertise in each of these areas of translational biomedical informatics. The team includes: Dr Isaac Kohane, who is a Professor of Pediatrics, directs the Children's Hospital Informatics Program and leads an NLM-supported national center for biomedical computing; Dr Ron Kikinis who is a Professor at Harvard Medical School, and the Robert Greenes Distinguished Director of Biomedical Informatics in the Department of Radiology at Brigham and Women's Hospital; and Dr John Quackenbush, who is a Professor of Biostatistics and Computational Biology and Professor of Cancer Biology at the Dana-Farber Cancer Institute. During the K22 award program, the applicant and his study team will develop informatics models and methods for Computational Pathology data. In Aim 1, they will develop a Computational Pathology Ontology to support a Computational Pathology Knowledgebase. The knowledgebase will be populated with microscopic phenotype data, gene expression data, and clinical outcomes data from over 2,500 cancer patients that underwent molecular profiling as part of The Cancer Genome Atlas project. In Specific Aim 2, they will develop and apply methods in machine learning to identify associations between gene expression and microscopic phenotypes. This information will be incorporated into the C-Path Knowledgebase. In Aim 3, they will use the C-Path Knowledgebase to build integrative prognostic models that jointly model quantitative morphological data and quantitative gene expression data to predict patient survival. The integrative prognostic models generated in breast, brain, kidney, and lung cancer will lead to more effective diagnostics for these malignancies. The central hypothesis for this application is that morphological and molecular data are inherently complementary, and the most biologically informative and clinically useful predictive models will incorporate information from both of these heterogeneous data types. This research will form the basis for an R01 application to further develop and validate the informatics methods and models developed in this project.

抽象的 申请人是贝丝以​​色列执事医学中心和哈佛医学院的助理教授。 申请人的研究计划着重于开发转化生物信息学的方法以整合 异质生物医学数据类型（基因组，转录组，微观，临床），使能够 开发改进患者的诊断和治疗疗法。申请人完成的最新项目 包括：用于构建预后模型的计算病理学（C-PATH）平台的开发 摘自微观图像数据（科学转化医学，2011年）；以及意义的发展 预后特征的分析方法用于识别强大的预后基因表达符号形式 临床注释的基因组数据（PLOS计算生物学，2013年）。 该K22奖项将为申请人提供实现以下目标所需的支持：（1） 成为设计和使用本体论来建模计算病理学数据的专家 计算病理知识基础； （2）成为综合预测建模的专家 计算病理数据，基因表达数据和临床结果数据； （3）开发和 管理转化生物信息学领域的独立研究职业。为了实现这些目标，申请人 已经组建了一组导师和合作者，在这些转化方面的每个领域中都有专业知识 生物医学信息学。该团队包括：儿科教授Isaac Kohane博士指导 儿童医院信息学计划，领导NLM支持的国家生物医学中心 计算；哈佛医学院教授罗恩·基基尼（Ron Kikinis）博士和罗伯特·格林斯（Robert Greenes） Brigham和妇女放射学系的生物医学信息学杰出主任 医院;生物统计学和计算生物学教授John Quackenbush博士， 达纳 - 法伯癌症研究所的癌症生物学教授。 在K22奖计划期间，申请人及其学习团队将开发信息学模型和方法 用于计算病理数据。在AIM 1中，他们将开发一个计算病理学本体，以支持 计算病理知识基础。知识库将用显微镜表型填充 数据，基因表达数据和临床结果来自2500多名癌症患者的数据 分子分析是癌症基因组图集项目的一部分。在特定目标2中，他们将开发和应用 机器学习中的方法鉴定基因表达与微观表型之间的关联。 此信息将纳入C-Path知识库。在AIM 3中，他们将使用C-Path 知识库建立综合预后模型，共同对定量形态数据进行建模和 定量基因表达数据可预测患者的存活。在 乳腺癌，脑，肾脏和肺癌将导致这些恶性肿瘤更有效的诊断。 该应用的中心假设是形态学和分子数据本质上是 互补的，以及最有用的信息和临床上有用的预测模型将合并 来自这两种异构数据类型的信息。这项研究将构成R01的基础 进一步开发和验证本项目开发的信息学方法和模型的应用。