Proteomic blood profiles obtained by totally blind biological clustering in stable and exacerbated COPD patients

Enríquez Rodríguez, César Jessé; Pascual Guàrdia, Sergi, 1979-; Casadevall Fusté, Carme; Caguana Vélez, Oswaldo Antonio; Rodríguez Chiaradia, Diego Agustín; Barreiro Portela, Esther; Gea Guiral, Joaquim

doi:http://dx.doi.org/10.3390/cells13100866

Proteomic blood profiles obtained by totally blind biological clustering in stable and exacerbated COPD patients

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Enríquez-Rodríguez CJ, Pascual-Guardia S, Casadevall C, Caguana-Vélez OA, Rodríguez-Chiaradia D, Barreiro E, et al. Proteomic blood profiles obtained by totally blind biological clustering in stable and exacerbated COPD patients. Cells. 2024 May 17;13(10):866. DOI: 10.3390/cells13100866

Abstract

Although Chronic Obstructive Pulmonary Disease (COPD) is highly prevalent, it is often underdiagnosed. One of the main characteristics of this heterogeneous disease is the presence of periods of acute clinical impairment (exacerbations). Obtaining blood biomarkers for either COPD as a chronic entity or its exacerbations (AECOPD) will be particularly useful for the clinical management of patients. However, most of the earlier studies have been characterized by potential biases derived from pre-existing hypotheses in one or more of their analysis steps: some studies have only targeted molecules already suggested by pre-existing knowledge, and others had initially carried out a blind search but later compared the detected biomarkers among well-predefined clinical groups. We hypothesized that a clinically blind cluster analysis on the results of a non-hypothesis-driven wide proteomic search would determine an unbiased grouping of patients, potentially reflecting their endotypes and/or clinical characteristics. To check this hypothesis, we included the plasma samples from 24 clinically stable COPD patients, 10 additional patients with AECOPD, and 10 healthy controls. The samples were analyzed through label-free liquid chromatography/tandem mass spectrometry. Subsequently, the Scikit-learn machine learning module and K-means were used for clustering the individuals based solely on their proteomic profiles. The obtained clusters were confronted with clinical groups only at the end of the entire procedure. Although our clusters were unable to differentiate stable COPD patients from healthy individuals, they segregated those patients with AECOPD from the patients in stable conditions (sensitivity 80%, specificity 79%, and global accuracy, 79.4%). Moreover, the proteins involved in the blind grouping process to identify AECOPD were associated with five biological processes: inflammation, humoral immune response, blood coagulation, modulation of lipid metabolism, and complement system pathways. Even though the present results merit an external validation, our results suggest that the present blinded approach may be useful to segregate AECOPD from stability in both the clinical setting and trials, favoring more personalized medicine and clinical research.