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; 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）是全球死亡的第四个主要原因，导致 巨大的公共卫生伯恩。 COPD的临床表现非常异构，并且 疾病病程受许多内源性和外源性因素的影响。寻找一群患者 类似的病理生物学对于准确预测疾病进展和发展至关重要 个性化治疗。目前，临床研究已在基于患者的歧视中分为 在其表型特征上，例如肺功能，加剧频率/强度，存在 肺气肿（临床亚型），或在其生物样品的分子组成上，如评估 通过多媒体测定法（分子亚型）。尽管对不同的分类提供了一些见解 COPD患者，这两种分类方法之间几乎没有达成协议。因此， 目前，病理生理过程，暴露及其表型后果之间的联系是 不清楚。在此应用中，我们建议使用深层神经网络体系结构来整合表型和 COPD受试者的基因组数据和构建综合患者概况，以描述表型和 病人的分子特征。这些轮廓将用于聚集患者以找到关节 COPD的临床和分子亚型（内型），并在5年时间内预测疾病预后 跨度。我们将提取每种内型的特征性临床和分子特征，以获得内型 - 特定的生物标志物并将其连接到COPD的临床表现。最后，我们将开发网络 - 理解与每种内型相关的关键分子途径和调节剂的方法。 实现本计划中提出的目标将需要一套独特的技能，这些技能涵盖生物学，网络 科学，机器学习和肺部疾病生物学。尽管Maiorino博士过去的职业轨迹有 为拟议的研究做好了很好的准备，促进我们当前对COPD异质性的理解是 具有挑战性的任务，需要在特定领域进行进一步培训。 Maiorino博士已经开发了一个综合 培训计划的重点是肺部疾病生物学，OMIC数据整合和高维 统计数据。 Maiorino博士将利用Channing部门提供的丰富的知识环境 网络医学和哈佛医学院参加课程并与他的定期会议 导师和顾问委员会成员。莫里诺博士的培训和研究计划总共使他能够 扩大他当前的技能，并发展成为一个独立的调查员，以促进进步 COPD的精密医学。