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Integrating Multi-Trait Genetics, Proteomics, and Deep Learning to Uncover Biomarkers and Therapeutic Targets for COPD Exacerbations

Award Number: R01HL185567
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 07/05/2026
PERIOD OF PERFORMANCE END DATE: 04/30/2030

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Integrating Multi-Trait Genetics, Proteomics, and Deep Learning to Uncover Biomarkers and Therapeutic Targets for COPD Exacerbations - PROJECT SUMMARY Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality. Exacerbations are episodes of acute worsening of symptoms and are a major contributor to illness, healthcare utilization, and cost. Yet, tools to accurately identify individuals at highest risk of exacerbations remain limited. Our preliminary data suggest that incorporating genetic risk from comorbid traits improves prediction of exacerbations. Additionally, protein biomarkers show promise for identifying at-risk individuals, and advances in genetic prediction methods can help pinpoint causal proteins and potential drug targets. Recent developments in deep learning applied to single-cell data now allow construction of lung-specific models that can map protein biomarkers to individual cell types and assess their therapeutic relevance. This project aims to improve both prediction and mechanistic understanding of COPD exacerbations by integrating multi-trait genetics, proteomics, and single-cell COPD lung data. In Aim 1, we will develop a multi-trait polygenic risk score by integrating genetic information across a wide range of traits and evaluate its ability to predict exacerbations across multiple large cohorts. We will also identify shared genetic variants that underlie exacerbation-related traits. In Aim 2, we will identify genetically predicted blood proteins associated with frequent exacerbations, including in a novel cohort of individuals with severe COPD with and without frequent exacerbations (≥ 2 per year versus none). In Aim 3, we will apply a deep learning model trained on COPD lung single-cell data to prioritize protein targets most likely to be druggable in specific COPD lung cell types. This integrative approach will improve risk prediction, uncover biological pathways underlying COPD exacerbations, and support the development of targeted therapies.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $894,314 )
2026	2026	BRIGHAM & WOMENS HOSPITAL INC	75 FRANCIS ST	BOSTON	MA	02115	SUFFOLK	USA	Lung Diseases Research	000	1	7/8/2026	NEW	$894,314
														Subtotal = $894,314

Grand Total All Awards = $894,314

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Integrating Multi-Trait Genetics, Proteomics, and Deep Learning to Uncover Biomarkers and Therapeutic Targets for COPD Exacerbations

Award Number: R01HL185567

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 07/05/2026

PERIOD OF PERFORMANCE END DATE: 04/30/2030

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