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， 巨大的公共卫生负担。慢性阻塞性肺病的临床表现是非常异质性的， 病程受多种内源性和外源性因素影响。发现患有以下疾病的患者群体 相似的病理生物学对于准确预测疾病进展和 个性化治疗。目前，临床研究已在区分患者的基础上 根据其表型特征，如肺功能、加重频率/强度、是否存在 肺气肿（临床亚型），或其生物样品的分子组成，如评估 通过多组学分析（分子亚型）。尽管提供了一些关于不同群体的见解， 对于COPD患者，这两种分类方法之间几乎没有一致性。因此， 目前，病理生理过程、暴露及其表型后果之间的联系 不清楚在该应用中，我们提出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 患者的分子特征。这些配置文件将用于对患者进行聚类， COPD的临床和分子亚型（内型），并预测5年内的疾病结局 跨度。我们将提取每个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，并将它们与COPD的临床表现联系起来。最后，我们将发展网络- 的方法来了解关键的分子通路和调节与每一个内型。 实现本计划中提出的目标将需要一套独特的技能， 科学、机器学习和肺病生物学。尽管马约里诺博士过去的职业轨迹 他为拟议的研究做好了充分的准备，推进了我们目前对COPD异质性的理解， 具有挑战性的任务，需要在具体领域进一步培训。Maiorino博士开发了一种全面的 培训计划侧重于肺部疾病生物学，组学数据整合， 统计Maiorino博士将利用Channing Division提供的丰富的知识环境 参加网络医学和哈佛医学院的课程，并参加定期会议， 导师和顾问委员会成员。总之，Maiorino博士的培训和研究计划将使他能够 扩大他目前的技能，并发展成为一个独立的调查员，促进 COPD的精准治疗