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.

摘要 申请者是贝丝以色列女执事医学中心和哈佛医学院的助理教授。 申请者的研究计划侧重于开发翻译生物信息学的方法，以整合 不同的生物医学数据类型(基因组、转录组、显微、临床)使 为患者改进诊断和治疗方法的发展。申请人最近完成的项目 包括：开发用于建立预后模型的计算病理学(C-PATH)平台 来自微观图像数据(《科学转化医学》，2011)；及其意义的发展 用于识别稳健的预后基因表达签名形式的预后签名方法分析 临床注释基因组数据(《公共科学图书馆·计算生物学》，2013)。 这项K22奖励将为申请者提供必要的支持，以实现以下目标：(1) 成为设计和使用本体来为计算病理学数据建模并支持 计算病理学知识库；(2)成为综合预测建模的专家 计算病理学数据、基因表达数据和临床结果数据；以及(3)开发和 管理翻译生物信息学方面的独立研究生涯。为了实现这些目标，申请者 我组建了一个团队，由在这些翻译领域拥有专业知识的导师和合作者组成 生物医学信息学。该团队包括：儿科学教授Isaac Kohane博士领导 儿童医院信息学计划和领导一个由NLM支持的国家生物医学中心 计算机、哈佛医学院教授Ron Kikinis博士和Robert Greenes 布里格姆妇女医院放射科生物医学信息学杰出主任 约翰·奎肯布什博士，他是生物统计学和计算生物学教授， 达纳-法伯癌症研究所的癌症生物学教授。 在K22奖励计划期间，申请者和他的学习团队将开发信息学模型和方法 用于计算病理学数据。在目标1中，他们将开发计算病理学本体论来支持 计算病理学知识库。知识库将用微观表型填充 来自2500多名癌症患者的数据、基因表达数据和临床结果数据 分子图谱作为癌症基因组图谱项目的一部分。在具体目标2中，它们将开发和应用 在机器学习中确定基因表达和微观表型之间的联系的方法。 此信息将并入C-Path知识库。在Aim 3中，他们将使用C-Path 用于构建综合预测模型的知识库，该模型联合对定量形态数据和 定量的基因表达数据来预测患者的生存。中生成的综合预测模型 乳腺癌、脑癌、肾癌和肺癌将导致对这些恶性肿瘤的更有效的诊断。 这一应用的中心假设是形态和分子数据本质上是 互补的，最具生物信息性和临床实用的预测模型将包括 来自这两种异类数据类型的信息。这项研究将构成R01的基础 应用程序进一步开发和验证本项目中开发的信息学方法和模型。