Cancer Deep Phenotype Extraction from Electronic Medical Records

描述(由申请人提供)：需要精确的表型信息来推进转化型癌症研究，特别是要揭示遗传、表观遗传和其他因素对肿瘤行为和反应性的影响。癌症表型变量的例子包括：肿瘤形态(例如组织病理学诊断)、共病情况(例如相关免疫疾病)、实验室结果(例如基因扩增状态)、特定的肿瘤行为(例如转移)和治疗反应(例如化疗药物对肿瘤的影响)。目前用于将EMR数据与组学数据相关联的模型在很大程度上忽略了临床文本，临床文本仍然是癌症患者表型信息的最重要来源之一。解锁临床文本的价值有可能使人们能够对癌症的起始、进展、转移和治疗反应有新的见解。我们建议两个具有长期开发开源自然语言处理(NLP)软件的历史的成熟信息学小组(APACHE cTAKES、CATIES和ODIE)进一步合作，以新方法扩展现有软件，用于癌症深度表型鉴定。几个目标提出了生物医学信息提取的研究，以前很少或根本没有工作(例如，临床基因组实体和因果话语)。还强调了提取数据的可视化、软件的可用性和传播。由乳腺癌、黑色素瘤和卵巢癌方面经验丰富的翻译调查人员领导的三个驱动肿瘤学项目将推动该软件的开发。这些实验室将为提取、测试软件的实用性和可用性贡献表型变量，并为外部评估提供环境。这项拟议的研究在从临床文本中自动提取癌症深层表型的新方法与表型知识表示和NLP的新兴标准之间架起了桥梁。这项工作与最近科学文献中的呼吁高度一致，即为精确医学和翻译研究提出可扩展和稳健的提取和表示表型的方法。

DESCRIPTION (provided by applicant): Precise phenotype information is needed to advance translational cancer research, particularly to unravel the effects of genetic, epigenetic, and othe factors on tumor behavior and responsiveness. Examples of phenotypic variables in cancer include: tumor morphology (e.g. histopathologic diagnosis), co-morbid conditions (e.g. associated immune disease), laboratory findings (e.g. gene amplification status), specific tumor behaviors (e.g. metastasis) and response to treatment (e.g. effect of a chemotherapeutic agent on tumor). Current models for correlating EMR data with -omics data largely ignore the clinical text, which remains one of the most important sources of phenotype information for cancer patients. Unlocking the value of clinical text has the potential to enable new insights about cancer initiation, progression, metastasis, and response to treatment. We propose further collaboration of two mature informatics groups with long histories of developing open-source natural language processing (NLP) software (Apache cTAKES, caTIES and ODIE) to extend existing software with new methods for cancer deep phenotyping. Several aims propose investigation of biomedical information extraction where there has been little or no previous work (e.g. clinical genomic entities, and causal discourse). Visualization of extracted data, usability of the software, and dissemination are also emphasized. Three driving oncology projects led by accomplished translational investigators in Breast Cancer, Melanoma, and Ovarian Cancer will drive development of the software. These labs will contribute phenotype variables for extraction, test utility and usability of the software, and provide the setting for a extrinsic evaluation. The proposed research bridges novel methods to automate cancer deep phenotype extraction from clinical text with emerging standards in phenotype knowledge representation and NLP. This work is highly aligned with recent calls in the scientific literature o advance scalable and robust methods of extracting and representing phenotypes for precision medicine and translational research.