Linking endotype and phenotype to understand COPD heterogeneity via deep learning and network science

通过深度学习和网络科学将内型和表型联系起来以了解 COPD 异质性

• 批准号: 10569732
• 负责人: Enrico Maiorino
• 金额: $ 17.82万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10569732
• 关键词: Affect; Agreement; Area; Biological; Biological Assay; Biological Markers; Biological Process; Biology; Cause of Death; Characteristics; Chronic Bronchitis; Chronic Obstructive Pulmonary Disease; Classification; Clinical; Clinical Data; Clinical Research; Cohort Studies; Collection; Data; Development; Dimensions; Discrimination; Disease; Disease Management; Disease Outcome; Disease Progression; Endogenous Factors; Environment; Environmental Risk Factor; Exogenous Factors; Frequencies; Future; Genes; Genetic; Goals; Grouping; Individual; Investigation; Joints; Knowledge; Link; Lung diseases; Machine Learning; Measurement; Medicine; Mentors; Methodology; Methods; Molecular; Molecular Conformation; Molecular Profiling; Multiomic Data; Network-based; Outcome; Pathogenesis; Pathway interactions; Patients; Pattern; Phenotype; Population; Process; Property; Public Health; Pulmonary Emphysema; Regulator Genes; Research; Research Personnel; Respiratory Disease; Sampling; Science; Spirometry; Structure of parenchyma of lung; Time; Training; Training Programs; Work; autoencoder; career; clinical biomarkers; clinical subtypes; clinically significant; data integration; deep learning; deep neural network; disease heterogeneity; disorder subtype; epigenomics; genomic data; high dimensionality; improved; insight; learning strategy; medical schools; meetings; member; molecular marker; molecular subtypes; mortality; multiple omics; neural network architecture; novel marker; personalized medicine; personalized therapeutic; phenotypic data; precision medicine; predict clinical outcome; profiles in patients; prognostic model; programs; protein protein interaction; pulmonary function; skills; specific biomarkers; statistics; transcription factor; transcriptomics

Summary/Abstract Chronic obstructive pulmonary disease (COPD) is the 4th leading cause of death worldwide, resulting in an immense public health burden. The clinical manifestations of COPD are extremely heterogeneous, and disease course is affected by numerous endogenous and exogenous factors. Finding groups of patients with similar pathobiology is crucial for the accurate prediction of disease progression and the development of personalized treatments. Currently, clinical research has been divided in the discrimination of patients based on either their phenotypic features, such as lung function, exacerbation frequency/intensity, presence of emphysema (clinical subtyping), or on the molecular compositions of their biological samples, as assessed through multi-omics assays (molecular subtyping). Despite providing some insights on different groupings of COPD patients, little agreement has been found between these two classification approaches. As such, the connection between pathophysiological processes, exposures, and their phenotypic consequences is currently unclear. In this application we propose to use deep neural network architectures to integrate phenotypic and genomic data of COPD subjects and construct integrated patient profiles that describe both the phenotypic and molecular features of the patient simultaneously. These profiles will be used to cluster patients to find joint clinical and molecular subtypes (endotypes) for COPD and to predict disease outcomes across a 5-year time span. We will extract the characteristic clinical and molecular features of each endotype to obtain endotype- specific biomarkers and connect them to clinical manifestations of COPD. Finally, we will develop network- based approaches to understand the key molecular pathways and regulators associated with each endotype. Achieving the objectives proposed in this plan will require a unique set of skills that span biology, network science, machine learning, and lung disease biology. Although Dr. Maiorino’s past career trajectory has prepared him well for the proposed research, advancing our current understanding of COPD heterogeneity is a challenging task that will require further training in specific areas. Dr. Maiorino has developed a comprehensive training program focusing on pulmonary disease biology, omics data integration, and high-dimensional statistics. Dr. Maiorino will take advantage of the rich intellectual environment offered by the Channing Division of Network Medicine and Harvard Medical School to attend courses and participate in regular meetings with his mentors and advisory board members. Altogether, Dr. Maiorino’s training and research plan will enable him to expand his current skillset and to develop into an independent investigator contributing to the advancement of precision medicine in COPD.

摘要/摘要 慢性阻塞性肺疾病（COPD）是全球第四大死亡原因，导致 COPD 的临床表现极其异质，并且造成巨大的公共卫生负担。 疾病进程受到多种内源性和外源性因素的影响。 相似的病理学对于准确预测疾病进展和发展至关重要 目前，临床研究已分为基于患者的歧视。 其表型特征，例如肺功能、恶化频率/强度、存在 肺气肿（临床亚型），或根据其生物样本的分子组成进行评估 尽管提供了一些关于不同分组的见解。 对于 COPD 患者，这两种分类方法之间几乎没有达成一致。 目前，病理生理过程、暴露及其表型后果之间的联系 在此应用中，我们建议使用深度神经网络架构来整合表型和 COPD 受试者的基因组数据并构建描述表型和症状的综合患者档案 同时分析患者的分子特征，这些特征将用于对患者进行聚类以寻找关节。 COPD 的临床和分子亚型（内型）并预测 5 年时间内的疾病结果 我们将提取每种内型的临床和分子特征，以获得内型- 最后，我们将开发网络-特定的生物标志物并将其与慢性阻塞性肺病的临床表现联系起来。 基于方法来了解与每种内型相关的关键分子途径和调节因子。 实现该计划中提出的目标将需要一套涵盖生物学、网络的独特技能 尽管 Maiorino 博士过去的职业轨迹已经改变了。 他为拟议的研究做好了充分的准备，推进我们目前对慢性阻塞性肺病异质性的理解是一个 这项具有挑战性的任务需要在特定领域进行进一步的培训。Maiorino 博士开发了一套全面的方法。 培训项目重点关注肺部疾病生物学、组学数据集成和高维 Maiorino 博士将利用钱宁分部提供的丰富的智力环境。 网络医学和哈佛医学院的教授参加课程并参加与他的定期会议 总之，Maiorino 博士的培训和研究计划将使他能够 扩展他目前的技能，并发展成为一名独立调查员，为推动科学发展做出贡献 慢性阻塞性肺病的精准医学。