Cancer Deep Phenotype Extraction from Electronic Medical Records

摘要 需要精确的表型信息来推进转化型癌症研究，特别是为了揭开 遗传、表观遗传和系统变化对肿瘤行为和反应的影响。举例 癌症的表型变量包括：肿瘤形态(例如组织病理学诊断)、共病 疾病(例如相关免疫疾病)、实验室发现(例如基因扩增状态)、特定肿瘤 行为(如转移)和治疗反应(如化疗药物对肿瘤的影响)。 目前用于将EMR数据与组学数据关联的模型在很大程度上忽略了临床文本，这仍然是 癌症患者最重要的表型信息来源。解锁临床文本的价值 有可能对癌症的发生、发展、转移和反应有新的洞察力 治疗。我们建议进一步合作，用新的癌症治疗方法来增强DeepPhe平台 深入的表型。几个目标提出了对生物医学信息提取的研究 以前的工作很少或根本没有(例如临床基因组学)。提取数据的可视化、软件的可用性、 并强调传播的重要性。由已完成的翻译领导的一系列不同的肿瘤学研究 乳腺癌、黑色素瘤、卵巢癌、结直肠癌和弥漫性大B细胞的研究 淋巴瘤将展示该软件的实用性。这些实验室将为以下项目贡献表型变量 提取、测试软件的实用性和可用性，并提供外部评估的设置。这个 拟议的研究为从临床文本中自动提取癌症深层表型的新方法搭建了桥梁 表型知识表示和自然语言处理的新兴标准。这项工作与最近的 呼吁在科学文献中提出可扩展和健壮的提取和表示方法 精确医学和翻译研究的表型。

Summary Precise phenotype information is needed to advance translational cancer research, particularly to unravel the effects of genetic, epigenetic, and systems changes 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 to enhance the DeepPhe platform 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). Visualization of extracted data, usability of the software, and dissemination are also emphasized. A diverse set of oncology studies led by accomplished translational investigators in Breast Cancer, Melanoma, Ovarian Cancer, Colorectal Cancer and Diffuse Large B-cell Lymphoma will demonstrate the utility of the software. These labs will contribute phenotype variables for extraction, test utility and usability of the software, and provide the setting for an 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 to advance scalable and robust methods of extracting and representing phenotypes for precision medicine and translational research.