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.

精确医学的一个目标是根据药物为个别患者选择最佳疗法 生物标志物以及疾病症状/体征1-3。该诊所已开始治疗患者 关于生物标志物。例子包括用于治疗突变EGFR肺癌患者的吉非替尼 vemurafenib用于治疗BRAF V600E突变的黑色素瘤患者。临床试验 还针对特定生物标志物的招募患者进行了量身定制。各种各样 已经进行了临床前研究以发现研究药物的生物标志物。最近的 细胞系和药物基因组学的大规模分子分析甚至可以预测 硅中的生物标志物。所有这些已确认或研究的生物标志物（在临床前，临床前， 诊所已成为现代翻译研究中的关键组成部分。但是，我们的 当前有关生物标志物的知识分散并锁定在不同的地方，包括 FDA标签，临床试验描述或出版物，列出了整合的重大障碍 他们进入知识图以增加推理。因此，我们建议创建一部小说 生物标志物发现的综合知识来源。这个新来源将提高质量 和毒品 - 生物标志物 - 疾病患者和合成新的连接数量 精确医学研究的知识。 遵守已建立的标准并帮助实施数据/软件标准 翻译者，我们将首先开发一个本体来定义生物标志物及其与其他人的关系 生物医学实体。接下来，我们将利用艺术状态深度学习方法提取 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的方法 使用大量的医学生手动检查和策划生物标志物优先 我们的机器学习模型。机器学习模型将通过 半监督的方法。为了确保提供的知识高质量，多行（在 在临床上，临床前的硅和置信度分数将与每个临床 生物标志物。通过与NCATS员工的合作，我们将将生物标志物链接到其他可用的 增加推理的资源。 我们希望资源将是知识图的关键组成部分，启用查询 与精密医学和AI模型建立有关的新颖问题。例如，可以 药物X在基因Z突变的小鼠模型中起作用？在哪个患者人群中可能会吸毒x 有效吗？可以将药物X重新用于治疗疾病M，而药物X的生物标志物为 呈现？我们可以在没有生物标志物的情况下找到那些患者的新药/靶标 批准的药物？此外，标记且曲线良好的数据以及分子曲线 为新颖的生物标志物发现提供AI-Ready Resources，可以通过长凳进一步验证 科学家。 为了实现目标，我们组建了一个杰出的团队，完成了生物学专家的杰出团队， 信息，机器学习，本体和知识图。通过两个NIH资助 生物标志物发现项目，Pi Chen博士在生物标志物方面拥有广泛的知识 发现，与台式合作者一起收集了引人注目的用例，并建立了一个工具以精确 药品。 Co-i Duesbery博士，培训和研究主任Spectrum Health的生物学家 系统，将使用现实数据来领导对生物标志物的检查。通过结合专业知识 克里希南博士（Krishnan）建立了深入学习方法来注释自由文本的疾病样本， 专门从事本体发展和知识图构建的丁博士以及 矿业，团队的位置很好，可以实现该项目的目标。 潜在的挑战包括招募域专家以验证生物标志物的广泛 疾病范围，与其他项目中提供的数据集成以及有限的数据 资源涵盖所有疾病。前两个部分中的探索性研究将使 更好地估计这些挑战。