Informatics Links Between Histological Features and Genetics in Cancer

癌症组织学特征与遗传学之间的信息学联系

• 批准号: 9278131
• 负责人: Kun Huang
• 金额: $ 2.39万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-18 至 2017-07-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9278131
• 关键词: Address; Adoption; Algorithmic Analysis; Algorithms; Area; Automobile Driving; Biological Markers; Biology; Cancer Biology; Cancer Etiology; Cancer Patient; Cells; Characteristics; Clinic; Clinical; Clinical Data; Communities; Complex; Computer software; DNA Sequence Alteration; Data; Data Analyses; Data Collection; Data Sources; Development; Diagnosis; Ecosystem; Epigenetic Process; Evaluation; Event; Fostering; Foundations; Future; Gene Expression; Gene Expression Profile; Genetic; Genetic Markers; Genomic approach; Genomics; Goals; Histologic; Image; Image Analysis; Informatics; Lead; Libraries; Link; Malignant Neoplasms; Measures; Methods; Modality; Molecular; Molecular Profiling; Morphology; Neoplasm Metastasis; Organ; Outcome; Pathologist; Phenotype; Recurrence; Research; Research Personnel; Sampling; Scheme; Software Tools; Staging; Subgroup; System; Systems Biology; Testing; The Cancer Genome Atlas; Tissues; Translational Research; Visual; Visualization software; anticancer research; base; biomarker discovery; cancer biomarkers; cancer genomics; cancer imaging; cancer subtypes; clinical biomarkers; clinical phenotype; data integration; data management; data visualization; design; effective therapy; genetic variant; genomic data; high throughput technology; histological image; image processing; insight; molecular imaging; molecular marker; novel; open source; outcome forecast; patient population; patient stratification; personalized medicine; phenotypic data; population stratification; public health relevance; software development; software systems; tool; treatment response; tumor

 DESCRIPTION (provided by applicant): Cancers are often highly heterogeneous with many different subtypes. These subtypes confer different outcomes including prognosis, response to treatments, recurrence, and metastasis. In addition, these subtypes are often associated with different genetic mutations, epigenetic events, gene expression profiles, molecular signatures, tissue and organ morphologies, and clinical phenotypes. Effective treatment requires a personalized characterization of genetic, molecular, and clinical biomarkers. Integrative genomics, where multiple data modalities are used to jointly stratify the patients into subtypes, holds the clear promise for enhancing the prediction of differential clinical outcomes and enabling personalized treatment schemes. Our primary goal is to develop an informatics platform enabling the discovery of integrative biomarkers (including multiple phenotypic and genomic data sources) that can effectively stratify patients into subtypes. Specifically we focus on integrating histological image data with other data modalities. Histological image data obtained from tumor samples provide critical information regarding the organizational and morphological features of the tumor which are used by pathologists to make diagnoses such as grading and staging. In addition, these cellular level morphological features are manifestations of molecular events and genomic characteristics of the cells, which are measured in genomic data. Therefor morphological features provide an important bridge between the clinical phenotypes and genomics data. However, the wide adoption of imaging data in cancer studies is challenged by the large data size and complex algorithms. To address this, we propose to develop an informatics system which enables integrative genomics with a focus on imaging genomics. The resulting software will be open source and freely available to research communities. We plan to achieve our goals via three specific aims. First, we will develop software libraries for integrating genomic data, histological images, and clinical data for cancer biomarker discovery and subtyping. Second, we will integrate the imaging analysis and data integration algorithms as well as data visualization tools into a high throughput data management system previously developed at OSU such that the biomedical researchers and clinicians can retrieve data and carry out such analysis without the need for repeatedly implementing complex systems. Finally, we will test the software by applying it on multiple different cancer studies for further evaluation. The system will be designed based on principles of open source software and will be disseminated to the research communities freely.

 描述（由申请人提供）：癌症通常具有高度异质性，具有许多不同的亚型。这些亚型赋予不同的结果，包括预后、对治疗的反应、复发和转移。此外，这些亚型通常与不同的基因突变、表观遗传事件、基因表达谱、分子特征、组织和器官形态以及临床表型相关。有效的治疗需要遗传、分子和临床生物标志物的个性化表征。整合基因组学，其中多种数据模式被用来联合分层的患者到亚型，持有明确的承诺，以加强预测的差异临床结果和实现个性化的治疗方案。我们的主要目标是开发一个信息学平台，能够发现整合的生物标志物（包括多个表型和基因组数据源），可以有效地将患者分为亚型。具体来说，我们专注于将组织学图像数据与其他数据模式相结合。从肿瘤样本获得的组织学图像数据提供了关于肿瘤的组织和形态学特征的关键信息，病理学家使用这些信息进行诊断，例如分级和分期。此外，这些细胞水平的形态学特征是细胞的分子事件和基因组特征的表现，其在基因组数据中测量。因此，形态学特征提供了临床表型和基因组学数据之间的重要桥梁。然而，在癌症研究中广泛采用成像数据受到大数据量和复杂算法的挑战。为了解决这个问题，我们建议开发一个信息系统，使整合基因组学的重点是成像基因组学。由此产生的软件将是开源的，并免费提供给研究界。我们计划通过三个具体目标来实现我们的目标。首先，我们将开发用于整合基因组数据、组织学图像和临床数据的软件库，用于癌症生物标志物的发现和分型。其次，我们将整合成像分析和数据集成算法以及数据可视化工具到一个高通量的数据管理系统，以前在俄勒冈州立大学开发的生物医学研究人员和临床医生可以检索数据，并进行这种分析，而不需要重复实施复杂的系统。最后，我们将通过将其应用于多个不同的癌症研究来测试该软件，以进行进一步的评估。该系统将根据开放源码软件的原则设计，并将免费分发给研究界。