Drug biomarker resources for precise translational research

用于精准转化研究的药物生物标志物资源

• 批准号: 10056488
• 负责人: Bin Chen
• 金额: $ 5.82万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-24 至 2020-04-07
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10056488
• 关键词: Adopted; BRAF gene; Biological Markers; Biology; Cancer Patient; Cell Line; Clinic; Clinical Trials; Computer software; Data; Disease; Ensure; Epidermal Growth Factor Receptor; Funding; Gefitinib; Genes; Goals; Health system; Informatics; Investigation; Investigational Drugs; Knowledge; Label; Lead; Link; Machine Learning; Malignant neoplasm of lung; Manuals; Medical Students; Mining; Modeling; Modernization; Molecular Profiling; Mutate; Mutation; Ontology; Patient Recruitments; Patients; Pharmaceutical Preparations; Pharmacogenomics; Positioning Attribute; Publications; Research; Resources; Sampling; Scientist; Signs and Symptoms; Source; Supervision; Text; Training; Translational Research; United States National Institutes of Health; Work; base; biomarker discovery; crowdsourcing; data resource; deep learning; improved; in silico; individual patient; knowledge graph; learning strategy; melanoma; molecular scale; mouse model; mutant; new therapeutic target; novel; novel marker; ontology development; optimal treatments; patient population; pre-clinical; precision medicine; preclinical study; recruit; specific biomarkers; tool

One goal of precision medicine is to select optimal therapies for individual patients based on drug biomarkers as well as disease symptoms/signs 1–3. The clinic has started to treat patients based on biomarkers. Examples include Gefitinib used to treat lung cancer patients with mutant EGFR and Vemurafenib used to treat melanoma patients with the BRAF V600E mutation. Clinical trials have also been tailored to recruit patients with the presence of specific biomarkers. A variety of preclinical studies have been conducted to discover biomarkers of investigational drugs. Recent large-scale molecular profiling of cell lines and pharmacogenomics even enables the prediction of biomarkers in silico. All these confirmed or investigational biomarkers (in silico, preclinical, in clinic) have emerged as critical components in modern translational research. However, our current knowledge about biomarkers is scattered and locked away in different places, including FDA labels, clinical trial descriptions, or publications, presenting a significant barrier to integrating them into knowledge graphs to augment reasoning. Therefore, we propose to create a novel composite knowledge source for biomarker discovery. This new source will improve the quality and quantity of connections between drug-biomarker-disease-patient and synthesize new knowledge for precision medicine research. To comply with established standards and aid the implementation of data/software standards for Translator, we will first develop an ontology to define biomarkers and their relationships with other biomedical entities. Next, we will leverage state of the art deep learning methods to extract biomarkers from publications and clinical trials. We will further adopt a crowd-sourcing approach using a large pool of medical students to manually inspect and curate biomarkers prioritized by our machine learning models. The machine learning models will be iteratively improved through a semi-supervised approach. To ensure high quality of provided knowledge, multiple lines (in silico, preclinical, in clinic) of evidence along with confidence scores will be associated with each biomarker. Through collaborating with NCATS staff, we will link biomarkers to other available resources to augment reasoning. We expect that the resource will be a critical component of a knowledge graph, enabling the query of novel questions related to precision medicine and the building of AI models. For example, can drug x work in a mouse model y where gene z is mutated? In what patient population may drug x be effective? Can drug x be repurposed to treat condition m where the biomarker of drug x is presented? Can we find new drugs/targets for those patients with the absence of the biomarker for the approved drug? Moreover, the labeled and well-curated data along with molecular profiles provide AI-ready resources for novel biomarker discovery that could be further validated by bench scientists. To achieve the goal, we have assembled an outstanding team comprising experts in biology, informatics, machine learning, ontology, and knowledge graph. Through two NIH-funded biomarker discovery projects, PI Dr. Chen has gained extensive knowledge in biomarker discovery, collected compelling use cases with bench collaborators, and built a tool for precision medicine. Co-I Dr. Duesbery, a biologist by training and Director of Research at Spectrum Health System, will lead the inspection of biomarkers using real-world data. By combining expertise of Dr. Krishnan who has built deep-learning methods to annotate disease samples from free-text, and Dr. Ding who specializes in ontology development and knowledge graph construction and mining, the team is well-positioned to accomplish the goals of this project. Potential challenges include the recruitment of domain experts to validate biomarkers for a wide range of diseases, the integration with the data provided in other projects, and the limited data resources to cover all diseases. The exploratory study in the first two segments will enable a better estimation of these challenges.

精准医学的一个目标是根据药物为个别患者选择最佳治疗方法 生物标志物以及疾病症状/体征1-3。该诊所已经开始治疗基于 在生物标志物上。例如，吉非替尼用于治疗突变的EGFR肺癌患者 Vemurafenib用于治疗BRAF V600E突变的黑色素瘤患者。临床试验 也是为了招募存在特定生物标志物的患者而量身定做的。各种各样的 已经进行了临床前研究，以发现研究药物的生物标记物。近期 细胞系和药物基因组学的大规模分子图谱甚至使这种预测成为可能 硅胶中的生物标记物。所有这些已证实或正在研究的生物标志物(硅胶、临床前、 临床)已成为现代翻译研究的重要组成部分。然而，我们的 目前关于生物标志物的知识分散在不同的地方，包括 FDA的标签、临床试验描述或出版物，对整合构成了重大障碍 将它们转化为知识图，以增强推理。因此，我们建议创作一部小说 生物标志物发现的复合知识来源。这一新货源将提高质量。 以及药物-生物标记物-疾病-患者与合成新技术之间的联系 精准医学研究知识。 遵守既定标准并协助实施数据/软件标准 首先，我们将开发一个本体来定义生物标记物及其与其他生物标记物的关系 生物医学实体。接下来，我们将利用最先进的深度学习方法来提取 来自出版物和临床试验的生物标志物。我们将进一步采用众包方式。 使用大量医学生手动检查和管理生物标记物 我们的机器学习模型。机器学习模型将通过迭代进行改进 一种半监督的方法。为确保所提供知识的高质量，多条线路(在 电子计算机、临床前、临床中)的证据以及置信度分数将与每个 生物标志物。通过与NCATS工作人员的合作，我们将把生物标记物与其他可用的 增强推理的资源。 我们预计资源将是知识图谱的关键组件，从而支持查询 一系列与精确医学和人工智能模型建立有关的新问题。例如，可以 药物x在基因z突变的小鼠模型y中起作用吗？在哪些患者群体中可以使用药物x 是有效的？可以改变药物x的用途来治疗药物x的生物标记物所在的疾病m吗？ 赠送的？我们能否为那些缺乏生物标记物的患者找到新的药物/靶点 批准的药物吗？此外，标记和精心挑选的数据以及分子轮廓 为新的生物标记物发现提供人工智能就绪资源，可由BASE进一步验证 科学家们。 为了实现这一目标，我们组建了一支由生物学专家组成的优秀团队， 信息学、机器学习、本体论和知识图谱。通过NIH资助的两个 生物标记物发现项目，陈博士在生物标记物方面获得了广泛的知识 发现，与工作台合作者一起收集令人信服的用例，并构建了一个精确度工具 医药。Co-I杜斯贝里博士，一位训练有素的生物学家，光谱健康公司的研究主任 该系统将使用真实世界的数据领导对生物标志物的检查。通过将以下方面的专业知识结合起来 克里希南博士建立了深度学习方法，从自由文本中注释疾病样本， 和丁博士，他专门从事本体开发和知识图谱构建，并 通过挖掘，该团队处于有利地位，能够完成该项目的目标。 潜在的挑战包括招募领域专家来验证广泛的生物标记物 疾病的范围、与其他项目提供的数据的结合以及有限的数据 覆盖所有疾病的资源。前两个部分的探索性研究将使 更好地估计这些挑战。