Informatics Links Between Histological Features and Genetics in Cancer

 描述(申请人提供)：癌症通常是高度异质性的，有许多不同的亚型。这些亚型提供不同的结果，包括预后、治疗反应、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有关。有效的治疗需要对遗传、分子和临床生物标记物进行个性化表征。综合基因组学，其中使用多种数据模式来联合将患者分成不同的亚型，在增强对不同临床结果的预测和实现个性化治疗方案方面有着明确的前景。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成不同的亚型。具体地说，我们专注于将组织图像数据与其他数据形式相结合。从肿瘤标本中获得的组织图像数据提供了有关肿瘤组织和形态特征的关键信息，病理学家利用这些信息进行诊断，如分级和分期。此外，这些细胞水平的形态特征是细胞的分子事件和基因组特征的表现，这些特征是在基因组数据中测量的。因此，形态特征为临床表型和基因组学数据之间提供了重要的桥梁。然而，成像数据在癌症研究中的广泛采用受到数据量大和算法复杂的挑战。为了解决这一问题，我们建议开发一种信息学系统，使集成基因组学成为可能，重点是成像基因组学。由此产生的软件将是开源的，研究社区可以免费使用。我们计划通过三个具体目标来实现我们的目标。首先，我们将开发整合基因组数据、组织图像和临床数据的软件库，用于癌症生物标记物的发现和亚型划分。其次，我们将把成像分析和数据集成算法以及数据可视化工具集成到以前在俄亥俄州立大学开发的高通量数据管理系统中，以便生物医学研究人员和临床医生可以检索数据并执行此类分析，而不需要重复执行复杂的系统。最后，我们将通过在多个不同的癌症研究中应用该软件进行测试，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。

 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.