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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Subtyping Chronic Obstructive Pulmonary Disease Using Peripheral Blood Proteomics.

Sara Zarei^1,2, Ali Mirtar³, Jarrett D Morrow¹

¹Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

Chronic Obstructive Pulmonary Diseases (Miami, Fla.)

|August 30, 2017

Summary

Researchers identified three distinct subgroups within chronic obstructive pulmonary disease (COPD) using proteomic data. One subgroup showed less emphysema but potentially higher inflammation, suggesting new therapeutic targets for COPD patients.

Keywords:

biomarkers cluster analysis emphysema inflammation

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TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

Area of Science:

Pulmonology
Proteomics
Biomarker Discovery

Background:

Chronic obstructive pulmonary disease (COPD) is a complex, heterogeneous lung disorder with varied clinical presentations and disease trajectories.
Previous COPD subtyping efforts relied on clinical phenotypes, genetics, or limited biomarkers, necessitating advanced approaches for deeper understanding.
Proteomic analysis offers a powerful tool to uncover novel disease subtypes based on comprehensive molecular profiles.

Purpose of the Study:

To utilize proteomic data to identify distinct molecular subtypes of COPD within a cohort of clinically similar individuals.
To characterize the clinical and imaging features of identified COPD subgroups.
To explore potential therapeutic implications of the discovered COPD subtypes.

Main Methods:

Serum samples from 396 COPD patients in the Treatment of Emphysema with a gamma-Selective Retinoid Agonist (TESRA) study were analyzed using multiplex biomarker panels.
Hierarchical clustering was applied to the proteomic data (87 biomarkers) after missing value imputation to define COPD subgroups.
Quantitative CT scans and St. George's Respiratory Questionnaire scores were used to assess emphysema severity and quality of life, respectively.

Main Results:

Three distinct COPD subgroups were identified, with sizes of 67.4%, 26.3%, and 6.3%.
The smallest subgroup (6.3%) exhibited less emphysema on CT scans but reported a worse quality of life, despite similar lung function (FEV1) compared to other groups.
Biomarkers distinguishing this subgroup were associated with platelet alpha granule and cell chemotaxis pathways, suggesting an inflammatory component.

Conclusions:

Proteomic profiling can delineate clinically relevant COPD subtypes beyond traditional phenotyping.
A distinct COPD subgroup characterized by less emphysema but potentially increased inflammation was identified.
This subgroup may represent a target population for novel anti-inflammatory therapies in COPD management.